Compose a paper using the five sources attached. The paper should .docx

Compose a paper using the five sources attached. The paper
should summarize not PLAGARIZE all 5 articles regarding
electronic medical records. APA FORMAT AND USE THE
SOURCES GIVEN ONLY. MAKE SURE TO USE INTEXT
CITATION FOR THEESE SOURCE. PAPER SHOULD BE 6
PAGES LONG.
Financial Ratio Analysis Worksheet
Your Full Name:
Ahmed Alothman
2011
2010
2009
Basic Rules
Liquidity
Current Ratio
1.50
1.6
1.2
Should be >1.00

Quick Ratio
0.86
0.95
0.6
Good to see close to 1
Leverage
Debt to total asset ratio
0.19
0.19
0.26
Good to see less than 1
Debt to Equity ratio
1.003
1.03
1.35
Smaller is better
Activity
Inventory turnover
7.8
8.3

7
Higher turnover will be better --- Smaller inventory level will
increase the turnover!
Fixed asset turnover
3.3
3.2
3.2
Higher turnover will be better --- Smaller fixed assets level will
increase the turnover (Productivity of the fixed assets)!
Profitability
Gross profit margin
0.3
0.3
0.3
Higher is better (Lower cost of goods sold or Higher sales will
increase the margin) --- Strategic directions (Ex. Focusing on
sales quantity or Lean operations)
Operating profit margin
0.06
0.06
0.06
Higher is better – Operational efficiency will be indicated.
Better cost structure might increase this margin.
Net profit margin

0.04
0.03
0.03
Higher is better. Total profitability (Corporate profitability).
Check the interest expense and Discontinued operations.
Return on total Assets (ROA)
0.06
0.06
0.06
Higher is better. Consider EBIT and portion of total assets.
The total sales for each $1 of total assets.
Your own financial assessment / Analyses / Suggestions:
Liquidity of Staples:

Liquidity ratios are used to measures the ability of the company
to pay off its current liabilities.
Using current ratio it shows that staples can pay off its current
liabilities more than 1.50, 1.6, 1.2 times respectively and still
remain with enough. The company is stable in paying off its
current liabilities
Using quick ratio Staples can pay off its liabilities 86 percent,
95 percent and 60 percent respectively of its current liabilities.
Leverage of Staples:
Leverage measures the risk level. But for staples, the company's
assets are far more than its liabilities thus the company can be
able to access loan application since its ability to pay is far
better and stronger. The company is less risky.
Staples has a Debt to equity ratio of 1 which means that
investors and creditors have an equal stake in the company's
assets. Lower ratio shoes a more stable business. Creditors
always views a higher debt to equity as risky and the investors
have not funded the operations as the creditors have. The
company should try and look for ways to reduce on the Debt to
equity ratio.
Activity of Staples:
This measures efficiency on how Staples can control its stock.
Staples has a very good inventory control system. This company
can sell off its inventory more than 7 times in a single year.
The company generates three times more sales than the net book
value of its assets. I can only suggest the company to compare
its ratios with other similar companies to gauge its efficiency.
Profitability of Staples:
Higher ratios are always favorable which means that a company
is selling its inventory at a higher profit percentage. Staples can
pay off its inventory costs and still remain with a percentage of
its sales revenue to cover his operating costs.

Return on assets ratio is a profitability ratio that measures the
net income produced by total assets. It explains how a company
effective staple earns a return on its investments in assets.
Higher ratios are always more favorable to the investors. Every
dollar that is invested in staples assets every year produces 6%
of net income. I will advice staple to compare its return with
other companies in the same industry.
Operating profit margin is a key indicator for investors and
creditors to see how staples is supporting its operations.
higher operating margin is more favorable. As for Staples 94
percent on every dollar on sales is used to pay for variable
costs. This means that 6 percent remains to cover for all non-
operating expenses.
References
Altiman, Edward. 1968. Financial ratios, Discriminant Analysis
and the prediction of corporate Bankruptcy. The Journal of
Finance 23 (4): 589-609
Fairfield, Patricia, and Teri Lombardi Yohn. 2001. Using Asset
Turnover and Profit Margin to Forecast Changes in
Profitability. Review of Accounting Studies 6 (4): 371
Beaver, William. 1966. Financial Ratios as Predictors of
Failure. Journal of Accounting Research Supplement 4 (3): 71-
111
Running head: CHALLENGING CASE STUDY
CHALLENGING CASE STUDY
2
Monsanto is one of the organizations in the country that helps
farmers in the production of sustainable ways of ensuring that

correct control is available for the purpose of increasing the
production level within the given trends. Better harvesting
methods will also be considered as a method of finding out
diversified entities within a defined mechanism. There are
several opportunities and threats that will ensure that the
organization will be at a position of increasing the required
trends in the set matter. In addition, the used forces should be
initiated with regards to coming up with appropriate attitude
thus maintaining the needed results (International Workshop on
Enterprise Applications and Services in the Finance Industry &
In Lugmargy, 2015).
Presence of new varieties in the market is one of the
opportunity to be used by the organization. It is an essential
module for the organization to have a pool of diversified
entities that will be able to increase the given result for a
particular set up. With all this, auditing will be made easy and
appropriate report will be obtained within the set guidelines. It
is essential to improve the needed results with the anticipation
of finding out exactly the defined attitudes for a given
mechanism all the time. Based on the understanding given, the
correct measures are used for the control of the needed output
(Moore, 2002).
Finding out the correct market may be a threat to the
organization. Different roles should be played with the
anticipation of finding out the given methods all the time. It is
up to the organization to increase the market structures thus
providing a defined level of anticipation thus yielding the
correct guidelines. In addition, there should be a set level that
will ensure that the market trends are met.
References
International Workshop on Enterprise Applications and Services
in the Finance Industry, & In Lugmargy, A (2015). Enterprise
Applications and Services in the Finance Industry: 7th
International Worship, Financecom 2014, Sydney,
Australia, December 2014.

Moore, T. G. (2002). China in the World Market: Chinese
Industry and International Sources of reform in Post- Mao
Era. Cambridge,UK: Cambridge University Press.
Strategy Analysis and Choice
Chapter Six
Chapter Objectives
Describe a three-stage framework for choosing among
alternative strategies.
SWOT Matrix, BCG Matrix, and QSPM.
Identify important behavioral, political, ethical, and social
responsibility considerations in strategy analysis and choice.
Discuss the role of intuition in strategic analysis and choice.
Discuss the role of organizational culture in strategic analysis
and choice.
6-*
Copyright ©2013 Pearson Education, Inc. publishing as Prentice
Hall
Hall
The Strategy-Formulation Analytical Framework
6-*
Hall

Hall
A Comprehensive Strategy-Formulation FrameworkStage 1 -
Input Stage
summarizes the basic input information needed to formulate
strategies
consists of the EFE Matrix, the IFE Matrix, and the Competitive
Profile Matrix (CPM)
6-*
Hall
Hall
Matching Stage
focuses on generating feasible alternative strategies by aligning
key external and internal factors
techniques include the Strengths-Weaknesses-Opportunities-
Threats (SWOT) Matrix, the Strategic Position and Action
Evaluation (SPACE) Matrix, the Boston Consulting Group
(BCG) Matrix, the Internal-External (IE) Matrix, and the Grand
Strategy Matrix
6-*
Hall
Hall

Decision Stage
involves the Quantitative Strategic Planning Matrix (QSPM)
reveals the relative attractiveness of alternative strategies and
thus provides objective basis for selecting specific strategies
6-*
Hall
Hall
Matching Key External and Internal Factors to Formulate
Alternative Strategies
6-*
Hall
Hall
The Matching StageThe Strengths-Weaknesses-Opportunities-
Threats (SWOT) Matrix helps managers develop four types of
strategies:
SO (strengths-opportunities) Strategies
WO (weaknesses-opportunities) Strategies
ST (strengths-threats) Strategies
WT (weaknesses-threats) Strategies
6-*
Hall
Hall

The Matching StageSO Strategies
use a firm’s internal strengths to take advantage of external
opportunitiesWO Strategies
aim at improving internal weaknesses by taking advantage of
external opportunities
6-*
Hall
Hall
The Matching StageST Strategies
use a firm’s strengths to avoid or reduce the impact of external
threatsWT Strategies
defensive tactics directed at reducing internal weakness and
avoiding external threats
6-*
Hall
Hall
A SWOT Matrix for a Retail Computer Store
6-*
Hall
Hall

The Boston Consulting Group (BCG) MatrixBCG Matrix
graphically portrays differences among divisions in terms of
relative market share position and industry growth rate
allows a multidivisional organization to manage its portfolio of
businesses by examining the relative market share position and
the industry growth rate of each division relative to all other
divisions in the organization
6-*
Hall
Hall
The BCG Matrix
6-*
Hall
Hall
The BCG MatrixQuestion marks – Quadrant I
Organization must decide whether to strengthen them by
pursuing an intensive strategy (market penetration, market
development, or product development) or to sell themStars –
Quadrant II
represent the organization’s best long-run opportunities for
growth and profitability
6-*
Hall

Hall
The BCG MatrixCash Cows – Quadrant III
generate cash in excess of their needs
should be managed to maintain their strong position for as long
as possibleDogs – Quadrant IV
compete in a slow- or no-market-growth industry
businesses are often liquidated, divested, or trimmed down
through retrenchment
6-*
Hall
Hall
The BCG MatrixThe major benefit of the BCG Matrix is that it
draws attention to the cash flow, investment characteristics, and
needs of an organization’s various divisions
6-*
Hall
Hall
The Quantitative Strategic Planning Matrix (QSPM)Quantitative
Strategic Planning Matrix (QSPM)
objectively indicates which alternative strategies are best
uses input from Stage 1 analyses and matching results from
Stage 2 analyses to decide objectively among alternative
strategies

6-*
Hall
Hall
The Quantitative Strategic Planning Matrix (QSPM)
6-*
Hall
Hall
Steps in a QSPM
Make a list of the firm’s key external opportunities/threats and
internal strengths/weaknesses in the left column of the QSPM
Assign weights to each key external and internal factor
Examine the Stage 2 (matching) matrices, and identify
alternative strategies that the organization should consider
implementing
6-*
Hall
Hall
Steps in a QSPM (cont.)
Determine the Attractiveness Scores (AS)
Compute the Total Attractiveness Scores
Compute the Sum Total Attractiveness Score

6-*
Hall
Hall
Positive Features of the QSPMSets of strategies can be
examined sequentially or simultaneouslyRequires strategists to
integrate pertinent external and internal factors into the decision
processCan be adapted for use by small and large for-profit and
nonprofit organizations
6-*
Hall
Hall
Limitations of the QSPMAlways requires intuitive judgments
and educated assumptionsOnly as good as the prerequisite
information and matching analyses upon which it is based
6-*
Hall
Hall
A QSPM for a Retail
Computer Store
6-*

Hall
Hall
A QSPM for a Retail
Computer Store
6-*
Hall
Hall
Validating Laboratory Results in Electronic Health
Records
A College of American Pathologists Q-Probes Study
Peter L. Perrotta, MD; Donald S. Karcher, MD
� Context.—Laboratories must ensure that the test results
and pathology reports they transmit to a patient’s
electronic health record (EHR) are accurate, complete,
and presented in a useable format.
Objective.—To determine the accuracy, completeness,
and formatting of laboratory test results and pathology
reports transmitted from the laboratory to the EHR.
Design.—Participants from 45 institutions retrospective-
ly reviewed results from 16 different laboratory tests,

including clinical and anatomic pathology results, within
the EHR used by their providers to view laboratory results.
Results were evaluated for accuracy, presence of required
elements, and usability. Both normal and abnormal results
were reviewed for tests, some of which were performed in-
house and others at a reference laboratory.
Results.—Overall accuracy for test results transmitted to
the EHR was greater than 99.3% (1052 of 1059). There
was lower compliance for completeness of test results,
with 69.6% (732 of 1051) of the test results containing all
essential reporting elements. Institutions that had fewer
than half of their orders entered electronically had lower
test result completeness rates. The rate of appropriate
formatting of results was 90.9% (98 of 1010).
Conclusions.—The great majority of test results are
accurately transmitted from the laboratory to the EHR;
however, lower percentages are transmitted completely
and in a useable format. Laboratories should verify the
accuracy, completeness, and format of test results at the
time of test implementation, after test changes, and
periodically.
(Arch Pathol Lab Med. 2016;140:926–931; doi: 10.5858/
arpa.2015-0320-CP)
Laboratories must ensure the accuracy, completeness,
andusability of information that is transmitted to the
patient’s electronic health record (EHR). This information
includes the results of tests performed in-house and by
reference laboratories and reported by manual entry or
transferred from the laboratory information system (LIS) or
middleware programs. Thorough reviews of electronic test
results and transmission of test results across electronic

interfaces have become more important as health care
providers (HCPs) increasingly request laboratory tests using
computerized order entry and review most test results
within the EHR. This Q-Probes study is focused on the
electronic reporting of laboratory results and the appearance
of test results and narrative reports in the EHR.
Historically, laboratories have met regulatory require-
ments for verifying test result accuracy and completeness by
reviewing test results within the LIS. As hard-copy paper
reports have increasingly been replaced by electronic
reporting, HCPs manage test results and other aspects of
patient care within an integrated EHR.1,2 A number of
information technology (IT) requirements for test reporting
are set forth in the College of American Pathologists’
(CAP’s) Laboratory Accreditation Program Laboratory
General Checklist3 and the Code of Federal Regulations4;
however, the rate of compliance with these requirements is
poorly documented. This Q-Probes study was designed to
measure the accuracy, completeness, and usability of
electronically reported and transmitted test results, and to
assess laboratory practices regarding validation of electronic
test results and pathology reports.
MATERIALS AND METHODS
Participants in this CAP Q-Probes study retrospectively
reviewed
results from 16 different laboratory tests by directly viewing
results
within the EHR. If the laboratory transmitted results to more
than
one EHR, the results were reviewed in the EHR primarily used
by
providers to view laboratory results. Results were reviewed for

a
spectrum of laboratory tests that were selected to include tests
performed by most laboratories, tests often sent to reference
laboratories, tests with numeric and textual results, manually
entered results, automatically resulted (ie,
autoverified/autovali-
dated), and anatomic pathology reports, including surgical
pathology and cytology. These included: creatinine, corrected
platelet count, hemoglobin A1c, international normalized ratio,
blood bank antibody screen, blood culture with sensitivities,
estimated glomerular filtration rate, human immunodeficiency
virus (HIV-1) quantitative polymerase chain reaction assay,
serum
Accepted for publication December 23, 2015.
From the Department of Pathology, West Virginia University,
Morgantown (Dr Perrotta); and the Department of Pathology,
George
Washington University Medical Center, Washington, DC (Dr
Karcher).
The authors have no relevant financial interest in the products
or
companies described in this article.
Corresponding author: Peter L. Perrotta, MD, Department of
Pathology, West Virginia University, Morgantown, WV 26506-
9203
(email: [email protected]).
926 Arch Pathol Lab Med—Vol 140, September 2016 Validating
Electronic Laboratory Results—Perrotta & Karcher
mailto:[email protected]

protein electrophoresis, Epstein-Barr virus viral-capsid antigen
immunoglobulin (Ig) G antibody, factor V Leiden mutational
assay,
breast estrogen receptor studies, surgical pathology report with
synoptic component, Papanicolaou cytology report with human
papillomavirus result, second-trimester maternal (Quad) screen,
and heparin-dependent antibody testing. Point-of-care test
results
and preliminary nonverified results were excluded.
Worksheets were provided to participants to facilitate data
collection. One normal and one abnormal (ie, outside of
reference
range) test result were identified for each test using sources
where
this information was most readily available (eg, LIS, log books,
reference laboratory reports, etc). Recent results were selected
whenever possible. Using patient identifiers, the test results
were
located within the EHR. Surgical pathology and cytology
reports
were also reviewed in the electronic system used by providers.
Because there are often several ways for providers to view
laboratory
results within an EHR, participants were asked to select the
results
screen used by most providers. Participants followed their local
policies for accessing patient information within the EHR.
Test result accuracy was assessed by comparing the numeric or
textual result in the EHR to the result in the LIS, paper
worksheets,
instruments, or other primary source. Test result completeness
was
determined by verifying the presence of the following result

components: (1) name/address of performing laboratory, (2)
linkage of result to the physician of record, (3) date/time of
specimen collection, (4) date/time of test result/report, (5)
specimen
source, (6) units of measurement when applicable, (7) result
interpretation when applicable, (8) information regarding
condition
and disposition of suboptimal specimens (eg, specimen
suitability),
(9) reference intervals appropriate for patient age/sex when
relevant, (10) flagging appropriate to clearly indicate abnormal
results (eg, color highlighting, up/down arrows, exclamation
marks,
etc), (11) audit trail for corrected results, (12) limitations of
test, and
(13) Food and Drug Administration disclaimer when applicable.
Result formatting was assessed by reviewing the result on the
EHR
computer screen and on a paper printout of the EHR report. The
appropriateness of result formatting was judged using the
following
criteria:
1. Appropriate: All results, reference information, and other
report
elements are presented in a visually easily understandable
format, with report elements easily located, clearly labeled, and
properly aligned.
2. Appropriate with minor defects: Results are presented with
minimal defects. For example, a report with a minor misalign-
ment or formatting problem that does not render the result
difficult to read is considered as having a minor defect.
3. Inappropriate: Results missing one or more reporting

elements
and/or presented in a way that is difficult for an HCP to
interpret, including major misalignment of results and/or
inclusion of extraneous information.
Participants were asked to answer survey questions regarding
their IT capabilities and practices. Individual associations
between
the frequency metrics and time intervals with the demographic
and
practice variables were analyzed using Kruskal-Wallis tests for
discrete-valued independent variables and regression analysis
for
continuous independent variables. Variables with significant
associations (P , .10) were then included in a forward-selection
multivariate regression model. A significance level of .05 was
used
for this final model. For the aggregate case results analyses, t
tests
were used. All analyses were performed using SAS v9.2 (SAS
Institute, Cary, North Carolina).
RESULTS
Performance Indicators
Three major performance indicators were determined
for this study, including: (1) the percent of tests
accurately transmitted to the EHR, (2) the percent of test
results containing essential reporting elements, and (3)
the percent of test results transmitted in a usable format
(Table 1). Results are based on information provided by
45 participants who submitted test result transmission
data for more than 1000 test results in aggregate. These
included results that were transmitted to the EHR from
an instrument, LIS, paper worksheet, or other primary

source. Overall accuracy of result transmission to the
EHR was 99.3% (1052 of 1059). There was no difference
in the accuracy of result transmission between normal
Table 1. Performance Indicators Showing Electronic Health
Record (EHR) Result Correctness, Completeness,
and Usability for 45 Reporting Institutions
Performance Indicators
All Institutions: Percentiles
5th 10th 25th Median 75th 90th
Percent of tests accurately transmitted to the EHR 95.7 100.0
100.0 100.0 100.0 100.0
Percent of test results containing essential reporting elements
9.1 20.0 45.0 92.9 100.0 100.0
Percent of test results transmitted in a usable format 36.8 85.7
93.8 100.0 100.0 100.0
Table 2. Relationships Between the Primary Study Indicators
and Practice Characteristics
No. (%) of
Institutions
All Institutions: Percentiles
Test Results Containing
Essential Reporting Elements, %
Test Results With
Appropriate Formatting, %
10th Median 90th 10th Median 90th

Percent of orders entered electronically by
ordering provider (P ¼ .01)
�50% 11 (25.0) 15.4 28.6 100.0
.50% 33 (75.0) 34.5 93.8 100.0
Frequency of complaints regarding
formatting of test results within EHR
(P ¼ .001)
Rarely (,1 per month) 33 (78.6) 92.9 100.0 100.0
Very/somewhat often 9 (21.4) 0.0 93.8 100.0
Abbreviation: EHR, electronic health record.
Arch Pathol Lab Med—Vol 140, September 2016 Validating
Electronic Laboratory Results—Perrotta & Karcher 927
(99.4%; 526 of 529) and abnormal (99.2%; 526 of 530) test
results.
There was lower compliance for completeness of test
results in that only 69.6% (732 of 1051) of the results
reviewed contained all of the essential reporting elements.
The overall rate for appropriate formatting of test results was
90.9% (918 of 1010). Appropriately formatted results
included those that were correctly configured on the EHR
screen views and EHR paper printouts, and did not contain
irrelevant or incorrect information. There were 2 practice
characteristics that were significantly associated with the
major performance indicators (Table 2). First, institutions
that had 50% or less of test orders entered electronically by
providers had lower test result completeness rates. Second,
institutions that rarely received documented complaints
regarding the formatting of test results within the EHR had

a higher percent of test results that were appropriately
formatted.
Results Transmission Methods
Participants provided detailed information regarding their
transmission methods for tests performed in-house and at
reference laboratories (Table 3). Overall, for in-house–
performed tests, most laboratories used electronic means
to transfer data from the LIS to the EHR (61.8%; 261 of 422
reviewed results) or the data were manually entered into the
LIS (33.2%; 140 of 422 results). Results were less commonly
transmitted from middleware to the EHR (4.3%; 18 of 422
results) and were rarely entered directly into the EHR. The
frequency of manual entry of results may be due to the
spectrum of tests examined in this study, which included
surgical pathology and cytology reports (Table 4). Other
tests that were less frequently electronically interfaced
included HIV-1 quantitative polymerase chain reaction,
serum protein electrophoresis, and factor V Leiden poly-
merase chain reaction.
Most—84.8% (128 of 151)—of the results reviewed that
were received from reference laboratories were electroni-
cally transferred through the LIS to the EHR. Small numbers
of reference laboratory results were manually entered into
the LIS or scanned into the EHR (Table 5). As for in-house–
performed services, anatomic pathology reports were
frequently entered manually into the LIS or scanned into
the EHR.
Test Result Completeness and Formatting
Most of the test results reviewed by participants
contained all of the reporting elements considered

necessary for a complete report (Table 6). There was little
difference between the completeness of reporting elements
between normal and abnormal test results. The name/
address of the performing laboratory and date/time of test
result/report were the elements most frequently missing on
result review. More than 99% (1038 of 1045) of test results
Table 3. Test Result Transmission Methods for Tests
Performed In-House and at Reference Laboratories
No. (%)
In-house–performed test results
Electronic: instrument to LIS to EHR 261 (61.8)
Manual entry in LIS; electronic
transmission to EHR
140 (33.2)
Electronic transmission from middleware to
EHR
18 (4.3)
Manual entry in EHR 3 (0.7)
Reference laboratory performed test results
Electronic: reference laboratory to LIS to
EHR
128 (84.8)
Manual entry in LIS and electronic
transmission to EHR

11 (7.3)
Scanned, copy/paste, etc, into EHR 10 (6.6)
Electronic: reference laboratory to EHR 1 (0.7)
Manual entry in EHR 1 (0.7)
Abbreviations: EHR, electronic health record; LIS, laboratory
informa-
tion system.
Table 4. Transmission Methods for In-House–Performed
Laboratory Tests
Test
Transmission Method, % (No. of Labs)
Electronic: Instrument
to LIS to EHR
Manual Entry
in LIS
Electronic:
Middleware to EHR
Manual Entry
in EHR
Corrected platelet count 65.6 (21) 31.3 (10) 3.1 (1) 0.0
Creatinine 88.6 (39) 2.3 (1) 9.1 (4) 0.0
Estimated glomerular filtration rate 90.2 (37) 2.4 (1) 7.3 (3) 0.0
International normalized ratio 95.5 (42) 2.3 (1) 2.3 (1) 0.0
Hemoglobin A1c 83.7 (36) 11.6 (5) 4.7 (2) 0.0
Human immunodeficiency virus quantitative

PCR
50.0 (6) 41.7 (5) 8.3 (1) 0.0
Serum protein electrophoresis 47.4 (9) 42.1 (8) 5.3 (1) 5.3 (1)
Epstein-Barr virus viral-capsid antigen IgG
antibody
83.3 (10) 16.7 (2) 0.0 0.0
Factor V Leiden mutational assay (PCR) 33.3 (4) 58.3 (7) 8.3
(1) 0.0
Blood bank antibody screen 39.5 (15) 57.9 (22) 2.6 (1) 0.0
Blood culture with sensitivity results 64.1 (25) 30.8 (12) 5.1 (2)
0.0
Breast pathology result with estrogen receptor
studies
16.0 (4) 80.0 (20) 0.0 4.0 (1)
Anatomic pathology routine surgical result
with synoptic report
23.1 (6) 73.1 (19) 0.0 3.8 (1)
Papanicolaou cytology report with human
papillomavirus testing results
17.6 (3) 76.5 (13) 5.9 (1) 0.0
Second-trimester maternal screen 28.6 (2) 71.4 (5) 0.0 0.0
Heparin-dependent antibody 18.2 (2) 81.8 (9) 0.0 0.0
information system; PCR, polymerase chain reaction.

reviewed were appropriately formatted, whether the result
was observed in the EHR on a computer screen or a paper
printout of the EHR result (Table 7). There was no clear
difference in the appropriateness of formatting between
normal and abnormal test results. However, almost 9% (85
of 1027) of the test results reviewed on an EHR computer
screen contained extraneous information and/or informa-
tion that did not need to be reported. This extra
information can potentially complicate test result interpre-
tation by the HCP.
IT Practices
Participating institutions provided information regard-
ing their IT practices related to laboratory testing (Table
8). There was a broad distribution concerning the percent
of tests that were electronically ordered by the provider.
All laboratories used an LIS, and all institutions had
implemented an EHR. Approximately half of laboratories
also used middleware. Slightly more than half (55.8%; 24
of 43) of the institutions had test information (eg,
specimen type, patient preparation, turnaround time,
etc) available in the electronic ordering system. Most
(70.5%; 31 of 44) of the institutions reported that most
test results transmitted to the EHR were routed to an
‘‘inbox’’ or other electronic system used by the ordering
HCP. In addition, most (79.5%; 35 of 44) participants
reported that most point-of-care test results were
available in the EHR. Slightly more than half (54.5%; 24
of 44) of the participants stated that patients were able to

review their laboratory results within a personal health
record or other electronic patient portal.
Institution Demographics
Of the 45 institutions participating in the study, 41 (91%)
were from the United States. The remaining 4 institutions
were from Saudi Arabia (2), Brazil, and Canada. Within the
last 2 years prior to data collection, 77.8% (35 of 45) of
participating laboratories were inspected by the CAP. The
size of most hospitals with hospital-based laboratories
(69.8%; 30 of 43) was less than 300 occupied beds.
Approximately one-third of participating laboratories were
teaching hospitals that also trained pathology residents.
Overall, 45.5% (20 of 44) of institutions were located in cities
and 75.0% (33 of 44) were nongovernmental. Mean test
volumes for participating laboratories were 1 809 998 for
clinical pathology tests, 33 457 for surgical pathology tests,
and 14 744 for gynecologic cytology.
COMMENTS
This Q-Probes study focused on the electronic reporting
of laboratory results within the EHR. It is important for
laboratories to verify test result accuracy, completeness,
Table 5. Transmission Methods for Tests Performed at
Reference Laboratories
Testa
Transmission Method, % (No. of Labs)
Electronic: Reference
Lab to LIS to EHR

Manual Entry
in LIS
Scan, Copy/Paste,
etc to EHR
Electronic: Reference
Lab to EHR
Human immunodeficiency virus quantitative
PCR
83.3 (20) 8.3 (2) 8.3 (2) 0.0
Serum protein electrophoresis 95.2 (20) 0.0 4.8 (1) 0.0
Epstein-Barr virus viral-capsid antigen IgG
antibody
94.4 (17) 5.6 (1) 0.0 0.0
Factor V Leiden mutational assay (PCR) 86.4 (19) 0.0 9.1 (2)
4.5 (1)
Anatomic pathology routine surgical result
with synoptic report
50.0 (3) 33.3 (2) 16.7 (1) 0.0
Papanicolaou cytology report with human
papillomavirus testing results
72.7 (8) 18.2 (2) 9.1 (1) 0.0
Second-trimester maternal screen 85.0 (17) 5.0 (1) 10.0 (2) 0.0
Heparin-dependent antibody 84.6 (11) 7.7 (1) 7.7 (1) 0.0
Abbreviations: EHR, electronic health record; IgG,

immunoglobulin G; LIS, laboratory information system; PCR,
polymerase chain reaction.
a Only tests with at least 5 participant responses are
summarized in this table.
Table 6. Completeness of Test Result Report by Report Element
for Normal and Abnormal Test Results
Test Result Element
Percent (No.) Containing Element
Overall Normal Results Abnormal Results
Name/address of performing laboratory 87.4 (1057) 87.5 (527)
87.4 (530)
Result linked to the physician of record 99.3 (1061) 99.4 (529)
99.2 (532)
Date/time of specimen collection 97.4 (1058) 97.5 (527) 97.4
(531)
Date/time of test result/report 87.2 (1059) 86.6 (528) 87.8 (531)
Specimen source, when applicable 97.6 (539) 97.4 (266) 97.8
(273)
Unit of measurement, when applicable 99.9 (691) 100.0 (343)
99.7 (348)
Test interpretation, when applicable 99.2 (723) 99.4 (357) 98.9
(366)
Information regarding condition and disposition of
suboptimal specimens, if applicable
97.0 (233) 95.8 (118) 98.3 (115)
Reference intervals present and appropriate 97.1 (771) 96.9
(384) 97.4 (387)
Flagging appropriate 95.8 (697) 96.2 (288) 95.6 (409)
Audit trail for corrected result, when applicable 95.0 (320) 95.6

(158) 94.4 (162)
Limitations of test, when applicable 94.2 (326) 95.1 (164) 93.2
(162)
FDA disclaimer, when applicable 97.1 (137) 97.1 (70) 97.0 (67)
Abbreviation: FDA, Food and Drug Administration.
and usability within the EHR because HCPs frequently
manage test results and other patient information directly
within these systems. There are also several IT require-
ments for reporting laboratory test results that must be
met, as stipulated in the Code of Federal Regulations4 and
the CAP’s Laboratory Accreditation Program Laboratory
General Checklist3 (Table 9). The Code of Federal
Regulations requires that a manual or electronic system
be in place to ensure test results are reliably sent from the
point of data entry to the final report destination. This
requirement applies to both results transmitted via an
electronic interface and manually entered results, and
includes results for tests performed in-house and at
reference laboratories.
The IT requirements for test resulting can be broadly
categorized into ‘‘accuracy,’’ ‘‘completeness,’’ and ‘‘usabil-
ity’’ of test result information that is transmitted to the
EHR. The participants of this Q-Probes study documented
that 99.3% (1052 of 1059) of results reviewed were
accurately transmitted to the EHR. This finding is expected
in that most results are electronically, not manually,
entered. However, there was lower compliance for
completeness of test results, with only 69.6% (732 of

1051) of the test results containing all of the essential
reporting elements outlined above. Institutions at which
less than half of test orders are entered electronically had
lower test result completeness rates. This may be due to
less developed electronic interfaces at these institutions.
The report components that were most commonly missing
included the ‘‘name/address of performing lab’’ and the
‘‘date/time of test result/report.’’ The most important
information (eg, result, reference range, interpretation)
should be prominently displayed and not obscured by
other elements that are less important to the HCP (eg,
disclaimers, test methodology, etc). Reference intervals
were available within the EHR for 97.1% (749 of 771) of the
results reviewed. The required reporting elements also
apply to point-of-care test results.
Most laboratories in this study electronically transmitted
results from the performing instrument to the LIS and then to
the EHR for commonly performed tests, including platelet
counts, creatinine, estimated glomerular filtration rate, and
international normalized ratio. Tests that are performed less
frequently and/or may not be automated (eg, quantitative
HIV polymerase chain reaction, protein electrophoresis, and
factor V Leiden mutational assay) often required manual
result entry. Electronic interfaces were used to transmit most
(84.8%; 128 of 151) of the results reviewed for tests
performed at the participant’s primary reference laboratory.
A small number of laboratories manually entered referred
testing results into the LIS or scanned/copied results directly
into the EHR. The CAP Laboratory Accreditation Program
Checklist requirements (GEN.41440) stipulate that essential
elements of referred test results be reported by the referring
Table 7. Appropriateness of Test Result Formatting for Normal
and Abnormal Test Results

Test Result View Assessed
Percent (No.) Appropriate
Overall Normal Results Abnormal Results
Appropriate formatting on EHR computer screen 99.3 (1045)
99.4 (519) 99.2 (526)
Appropriate formatting on EHR paper printout 99.4 (1044) 99.6
(519) 99.2 (525)
EHR computer screen does NOT contain extraneous or
nonreportable information
91.7 (1027) 91.8 (513) 91.6 (514)
Abbreviation: EHR, electronic health record.
Table 8. Information Technology Practices and
Practices of Participating Institutions
Practice or Characteristic Percent (No.)
Types of information systems used by
institutiona
LIS 100.0 (44)
EHR 97.7 (43)
Middleware 54.5 (24)
Dedicated anatomic pathology information
system
40.9 (18)
Percent of laboratory orders entered
electronically by the ordering provider

10–40 22.7 (10)
41–80 25.0 (11)
81–90 22.7 (10)
91–100 29.5 (13)
System types used by providers to view
electronic laboratory resultsa
EHR 95.5 (42)
Web portals 56.8 (25)
iPads or other tablet devices 40.9 (18)
Smart phones (iPhone, Android, etc) 29.5 (13)
LIS 27.3 (12)
Frequency of documented complaints
received by the laboratory regarding the
formatting of test results within the EHR
Rarely (,1 per month) 77.3 (34)
Somewhat often (1–3 per month) 4.5 (2)
Very often (.3 per month) 18.2 (8)
Frequency of verification of test results to and
from the EHRa
At installation 75.0 (33)
When a problem is identified 63.6 (28)
At least once a year 47.7 (21)
At least twice a year 25.0 (11)
Otherb 22.7 (10)
Mechanism used by laboratory director to
ensure that the content of laboratory
reports electronically transmitted to the
EHR effectively communicates patient test
resultsa

Review of results in EHR at time of
implementation
79.5 (35)
Review of results in the EHR at least every
2 y
61.4 (27)
Director does not review test results within
the EHR
6.8 (3)
informa-
tion system.
a Multiple responses permitted.
b Other responses included after testing/upgrade (6), daily (1),
downtime (1), and random sampling (1).
laboratory as received from the reference laboratory, without
alternations that could affect clinical interpretation. Format-
ting of results can be altered when results are electronically
transmitted from an LIS to an EHR. For example, tables,
underlining, and alignment are often lost or transmitted
inaccurately during electronic transfer. This can be especially
problematic for surgical pathology and cytology reports that

are often prepared using text editors with richer formatting
features. In these cases, creating a report using an open
standard for electronic document exchange (eg, PDF format)
that can be directly viewed in the EHR without the need for
discrete data transfer will preserve formatting. Maintaining
consistency of formatting for test results may also improve
their usability. Laboratory IT specialists will need to develop
particularly rigorous strategies for formatting results from
newer genomics technologies.5
Electronic interfaces have enabled more rapid and
accurate transmission of laboratory results. However, it is
critical that laboratories review transmitted results to ensure
they are complete, accurate, and in a maximally usable
format. Laboratory results should be reviewed before going
live with a new interface that transmits results to the EHR,
when changes are made at the laboratory or EHR level that
could alter test resulting, and periodically. In this study, a
relatively low percentage of laboratories were shown to
verify the transmission of data to the EHR at least once a
year or every other year. The CAP Laboratory General
Checklist3 requires that the CAP laboratory director review
and approve the content and format of paper and electronic
patient reports at least every 2 years.
A strength of this study was that participants evaluated
both clinical and anatomic pathology results because the
content and format of these reports differ significantly.
However, the study did have limitations, the most important
being the variability of the persons who assessed the
reports. Participants did not have difficulty determining
whether a required reporting element was present or
absent, but this may not be true for the more subjective
assessments of report formatting and usability. Although
guidelines were provided as described in ‘‘Materials and
Methods’’ for judging these aspects, each participating

laboratory assessed the quality of its own reports; significant
variability between sites in the more subjective assessments
cannot be excluded. Furthermore, the background and
experience of evaluators could also influence how they
critiqued reports. Participants did not specify who at their
institution evaluated the reports (eg, pathologist, laboratory
personnel, technologist, clinician, etc), and it is possible that
more than one individual at a site was needed to review all
tests. Finally, some participants did not fully complete data
collection forms for all 16 tests. This frequently occurs in
multisite Q-Probes studies when participants have difficulty
finding requested information or do not fully understand the
data collection instructions.
The methods outlined in this study can be used to help
laboratories meet requirements for verifying laboratory test
resulting within an EHR. The verification process should
include careful scrutiny for the required reporting elements
described in this study that are considered best practices
and are required by regulatory agencies. In some medical
systems, it may be necessary to review electronic results in
more than one electronic system. For example, because
laboratories are increasingly providing patients access to
laboratory test information, the accuracy and usability of
these results within patient portals should also be
verified.6,7 In fact, many medical laboratories in the United
States are required to provide patient access to their
laboratory test results. Although patients are generally
satisfied when they can view their test results online, it
remains unclear whether patient access to laboratory
reports and other elements of their EHR improves the
quality and safety of health care.8 Medical and laboratory
professionals express concerns that patients may not fully
appreciate the implications of their test results. This is
particularly true for anatomic and cytology reports that are

inherently difficult for most patients to understand. Finally,
laboratories should also focus on the usability of test
resulting to reduce the risk of the HCP misinterpreting a
test result. This may require interaction with a medical
informatics officer or other clinicians who participate in
institutional IT design and implementation. Overall,
laboratory professionals should be actively involved in
the development and maintenance of electronic test
ordering9 and resulting systems10 in collaboration with
their IT specialists.
References
1. Elder NC, McEwen TR, Flach J, Pallerla H. The management
of test results
in primary care: does an electronic medical record make a
difference? Fam Med.
2010;42(5):327–333.
2. Natarajan K, Stein D, Jain S, et al. An analysis of clinical
queries in an
electronic health record search utility. Int J Med Inform.
2010;79(7):515–522.
3. College of American Pathologists’ Commission on
Laboratory Accredita-
tion. Laboratory General Checklist. Northfield, IL: College of
American
Pathologists; 04.21.2014.
4. Centers for Disease Control and Prevention. Clinical
Laboratory Improve-
ment Amendments of 1988; Final Rule. Atlanta, GA: Centers
for Disease Control
and Prevention; 1992. 42 CFR Part 493.1291 Laboratory
Requirement, Standard:

Test Report.
5. Haga SB, Mills R, Pollak KI, et al. Developing patient-
friendly genetic and
genomic test reports: formats to promote patient engagement
and understanding.
Genome Med. 2014;6(7):58.
6. Zikmund-Fisher BJ1, Exe NL, Witteman HO. Numeracy and
literacy
independently predict patients’ ability to identify out-of-range
test results. J Med
Internet Res. 2014;16(8):e187.
7. Colpaert K, Decruvenaere J. Computerized physician order
entry in clinical
care. Best Pract Res Clin Anaesthesiol. 2009;23(1):27–38.
8. De Lusignan S, Mold F, Sheikh A, et al. Patients’ online
access to their
electronic health records and linked online services: a
systematic interpretative
review. BMJ Open. 2014;4:e006021.
9. Ali Baddour A, Dablool AS, Al-Ghamdi SS. Improving
laboratory test-
ordering with information technology. Int J Clin Med.
2012;3:446–458.
10. Singh H, Thomas EJ, Sittig DF, et al. Notification of
abnormal lab test results
in an electronic medical record: do any safety concerns remain?
Am J Med. 2010;
123(3):238–244.
Table 9. Data Elements Required for Laboratory Test

Resulting
Test Result Element
Positive patient identifiers
Name and address of laboratory location where test was
performed
Test report date
Test performed
Specimen source, when appropriate
Test result, including measurement units and/or
interpretation when applicable
Information regarding the condition and disposition of
specimens that do not meet the laboratory’s acceptability
criteria
Copyright of Archives of Pathology & Laboratory Medicine is
the property of College of
American Pathologists and its content may not be copied or
emailed to multiple sites or
posted to a listserv without the copyright holder's express
written permission. However, users
may print, download, or email articles for individual use.
FRONTLINE PHARMACIST

270 AM J HEALTH-SYST PHARM | VOLUME 73 | NUMBER
5 | MARCH 1, 2016
The Frontline Pharmacist column gives staff pharmacists an
oppor-
tunity to share their experiences and pertinent lessons related to
day-to-day practice. Topics include workplace innovations,
coop-
erating with peers, communicating with other professionals,
dealing
with management, handling technical issues related to pharmacy
practice, and supervising technicians. Readers are invited to
submit
manuscripts, ideas, and comments to AJHP, 7272 Wisconsin
Ave-
nue, Bethesda, MD 20814 (301-664-8601 or [email protected]).
Implementation of active surveillance in electronic
health records at pediatric institutions
Pharmacotherapy services provided by clinical pharmacists
within the hospital setting have
demonstrated optimal patient care outcomes,
and the use of active surveillance programs has
helped to improve the care provided to patients.1
Active surveillance consists of a set of process-
es for the continued systematic compilation,
analysis, and interpretation of data on benefits
and harms. These active surveillance processes
help to identify, evaluate, and communicate pre-
viously unknown effects of healthcare products,
or new aspects of known effects. The aim of the
process is to harness any beneficial effects and
prevent or mitigate effects that may cause harm.2
Some current forms of active surveillance that are

widely accepted and used by hospital pharmacists
include the reporting of adverse drug reactions
and the optimization of antimicrobial therapy through an
antimicrobial stewardship program. The benefits of active
surveillance have been shown through the use of such
programs and others, such as the U.S. Vaccine Adverse
Event Reporting System and institution-specific programs
targeting pharmacokinetic drug monitoring and drug dos-
ing in organ dysfunction.3-7 However, many of these pro-
grams are not integrated into a single application, making
a comprehensive evaluation cumbersome. Fortunately,
with newer, widely utilized electronic health record (EHR)
systems, there is the potential for improvement in active
surveillance programs and their applicability in pharma-
ceutical care.
The implementation of an active surveillance pro-
gram in a pediatric population is particularly difficult,
because all necessary components of a variable popula-
tion must be generalized into a single program or process.
One of the biggest obstacles in developing a pediatric-
focused active surveillance process is the need to ac-
count for the changes in pharmacokinetic and pharma-
codynamic parameters associated with the growth and
development of this population.8 The clinical pharmacy
group at the Children’s Hospital of Philadelphia custom-
ized an electronic program based on its pediatric patient
population to provide an improved and more compre-
hensive approach to the prioritization and monitoring of
a patient’s drug therapy.
Background. The Children’s Hospital of Philadelphia is an
urban, tertiary care hospital with 535 beds (203 intensive
care and 46 surgical), a level 1 trauma center, and a level
3 neonatal intensive care unit. At the time of writing, 8

postgraduate year 2 specialty trained pediatric clinical
pharmacists provided care for this medically complex
population. The clinical pharmacy team previously used
a paper-based documentation system to provide ongoing
patient monitoring of therapeutic drug levels and organ
dysfunction and to notate other pertinent findings in the
ongoing care of patients. This paper documentation was
then passed among the clinical pharmacist staff to facil-
itate cross-coverage in times of colleague absence. This
tedious, time-consuming, and potentially error-prone
approach relied on the clinical pharmacist to ensure that
all laboratory values were transcribed properly, with no
pertinent values omitted. A user-friendly, interactive,
and customizable tool was necessary to help prioritize
patient care, provide the data necessary for continual
pharmacokinetic drug monitoring, identify and assess
organ dysfunction, and provide alerts for patient-
specific factors that require assessment by a clinical
pharmacist. With an average patient: clinical pharmacist
ratio of 60:1, a decision was made to take advantage of a
new process that would be integrated into the EHR and
mailto:ajhp%40ashp.org?subject=
AM J HEALTH-SYST PHARM | VOLUME 73 | NUMBER 5 |
MARCH 1, 2016 271
allow for the efficient and effective provision of quality
pharmaceutical care.
Methods. An existing tool within the integrated
EHR—the Electronic Pharmacy Acuity System (ERxAS,
Epic Systems)—was customized to take the place

of the previously descr ibed paper-based system
of active surveillance. The ERxAS allows for real-
time, active collection and analysis of data entered into
the EHR. To customize this system, the clinical pharma-
cy group, comprising pediatric specialists from general
pediatrics, intensive care (cardiac, neonatal, and pedi-
atric), oncology, solid organ transplantation, and drug
information, came to a consensus regarding the clinical
criteria that were most relevant to the provision of efficient
pharmaceutical care. An analysis of the selected criteria
within a patient’s medical record are presented in a single
screen for the clinical pharmacist to review (Figure). The
criteria included considerations suggestive of organ
impairment (acute rises in or elevated serum creatinine
[SCr] level, abnormal creatinine clearance, elevated
Child-Pugh score), drug level results requiring assessment
by a clinical pharmacist (antimicrobials, anticoagulants,
anticonvulsants, immunosuppressants, antineoplastics,
and cardiac medications), active anticoagulant orders,
pertinent patient-specific problems (positive serum or
urine human chorionic gonadotropin, ketogenic diet,
Q-T interval prolongation, renal replacement therapy,
extracorporeal membrane oxygenation), and active
pharmacy intervention notes (ongoing pharmacokinetic
monitoring assessments, active organ impairment or
patient-specific alert assessments, or nonspecific phar-
macist intervention assessments).
Each identified clinical criterion was assigned a
point value based on its potential impact on a patient’s
pharmacotherapy. The goal of assigning points was to
identify those patients with a higher acuity, as they
likely require a greater level of attention and clini-
cal pharmacist review and intervention. The clinical
pharmacy group chose the department-specific val-

ues and weighting based on existing values prepop-
ulated in the system as purchased from the vendor
and then altered the point value based on the opin-
ion of the clinical pharmacy staff. For example, one
clinical criterion is the presence of a drug level for a
predetermined group of medications. When a drug
level is reported in the patient’s chart, it triggers the
ERxAS to assign a point value determined by the re-
sult (i.e., more points are assigned for levels outside
of normal limits and fewer for those within normal
limits).
ERxAS features. The ERxAS list is displayed in a table for-
mat with the patient’s total score appearing first (Figure).
The system highlights a patient’s ERxAS score with one of
three colors to designate the patient’s level of acuity—high
Figure. A screenshot in the Electronic Pharmacy Acuity System,
or ERxAS. The clinical criteria are displayed across
the top of the screen, with individual patient total scores shown
on the left. The change in total score since the previous
review along with the time since that review is displayed side
by side. The components of a patient’s total score are
shown within the individual contributing criterion. Below the
patient scores, the patient-specific comment box is shown
with examples of information that can be shared among clinical
pharmacists.
272 AM J HEALTH-SYST PHARM | VOLUME 73 | NUMBER
5 | MARCH 1, 2016
(red), moderate (orange), or low (yellow)—to direct the

attention of the clinical pharmacist to patients with the
highest acuity first. Another column indicates the change
in a patient’s ERxAS score since a clinical pharmacist last
reviewed the profile; the time that has elapsed since that
review occurred is presented in another column. These
data notify the clinical pharmacist of acute changes in a
patient’s profile and allow for the rapid identification and
assessment of changes. This information also assists the
clinical pharmacist in maintaining a consistent workflow
by allowing for easier identification of patients who still
require an assessment. Furthermore, these data serve
as a notification to other clinical pharmacists that the
profile has been reviewed and does not require further
attention, which prevents duplication of work. Other
data specific to the ERxAS include patient identifiers
and the scoring criteria comprising the patient’s total
ERxAS score (Figure). The scoring criteria are separat-
ed into individual columns to easily identify the rea-
sons for an elevated total score. Any column, including
the total score, can be sorted from highest to lowest to
help prioritize patient review.
A detailed view of the patient’s overall score (the
ERxAS profile) is displayed when a specific patient is se-
lected, which shows the breakdown of the patient’s indi-
vidual scoring components. For example, if a patient has
a serum vancomycin level within normal range, a score
would be triggered in the ERxAS and a point value would
populate in the “Drug Level” column on the ERxAS list,
causing the patient’s total ERxAS score to rise. While
the ERxAS list only notates a drug level, the detailed
ERxAS profile will provide the reason for the patient’s
score such as a serum vancomycin level. By having all of
this information readily available in a single window di-
rectly linked to the patient’s EHR, the ERxAS helps aug-
ment the efficiency of the clinical pharmacy staff.

The ERxAS profile also was constructed to allow the
clinical pharmacist to input information in a patient-
specific comment box to facilitate internal communi-
cation and transition of care between clinical pharma-
cists. This communication method allows one clinical
pharmacist to cross cover for another with a basic un-
derstanding of the pertinent patient-specific pharmaco-
therapy information included in the comment field. For
example, a patient who undergoes cardiac bypass and
sustains a notable elevation in SCr will have an ERxAS
score assigned based on this change. The clinical phar-
macist who identifies cardiac bypass as the reason for the
elevated SCr level can note this in the comment box as
the causative factor for the increased ERxAS score and
the clinical significance of the elevation.
Numerous features of the ERxAS streamline patient
assessment by the clinical pharmacists. The ERxAS is
accessed within the hospital’s EHR system, enabling
the use of a window-in-window feature for faster iden-
tification of pertinent data without leaving the EHR.
There is no need to access multiple programs in multi-
ple windows, as all necessary data can be found in the
EHR. Real-time analysis of each patient is made possi-
ble through the continual updating of patient data, de-
creasing the chance of an error due to delayed or out-
dated data. Further, the ERxAS allows users to group
patients, such as those on a specific unit, into separate
lists in order to reduce search time.
Four electronic reports are generated by the EHR
throughout the day (one to identify patients taking an-
ticoagulant medications and three to report all drug
levels of medications requiring pharmacokinetic moni-

toring). These reports are used by the clinical pharmacy
staff in combination with the ERxAS to reduce the risk of
omission of patients requiring a clinical pharmacist’s as-
sessment. The reports also serve as a redundancy in the
case of an ERxAS reset or downtime, helping provide the
greatest level of surveillance for patients.
Results. The ERxAS system was implemented in Janu-
ary 2011 and fully replaced the paper documentation
system. From January to June 2014, the clinical phar-
macy staff used the ERxAS to identify and assess 6741
medication levels. In addition, 61 courses of heparin
therapy and 51 courses of warfarin therapy were iden-
tified, monitored, and adjusted. A total of 732 organ
impairment cases were identified and assessed in 514
patients, with further ongoing assessments provided
until the patients were discharged. Altogether, this
equates to a mean of 41 patient interventions daily.
It is important to note that these numbers represent
a conservative estimate, as many organ impairment
cases and anticoagulant treatment courses necessitate
additional ongoing clinical pharmacist interventions,
but only the initial identification and assessment were
captured in our reporting.
Potential barriers to implementation. There are some
potential barriers preventing other institutions from
utilizing an electronic active surveillance system. Most
importantly is an EHR system that is able to support a
similar scoring system. If an institution’s EHR system
can support an electronic active surveillance system,
the time necessary to develop criteria, customize the
program, and educate the staff on its use could be an-
other potentially major barrier. This barrier is likely due
to a lack of familiarity with the program, the ability to
gain consensus on appropriate criteria, and the ability to

build a template.
Conclusion. The integration of the active surveillance
program into the clinical pharmacy workflow has ben-
efited the care of patients at the Children’s Hospital of
Philadelphia. The clinical pharmacy staff reports in-
creased workflow efficiency and a decrease in wasteful
redundancies, and the time saved has been allotted for
AM J HEALTH-SYST PHARM | VOLUME 73 | NUMBER 5 |
MARCH 1, 2016 273
other clinical activities. The system allows for hospi-
talwide coverage on the weekends by a single clinical
pharmacist. Finally, the customization of the ERxAS
allows the program to stay current as clinical pharmacy
practice advances in providing optimal pharmaceutical
care.
1. Anderson SV, Schumock GT. Evaluation and justification
of clinical pharmacy services. Expert Rev Pharmacoecon
Outcomes Res. 2009; 9:539-45.
2. Aronson J, Hauben M, Bate A. Defining “surveillance” in
drug
safety. Drug Saf. 2012; 35:347-57.
3. Jha A, Laguette J, Seger A, Bates D. Can surveillance
systems
identify and avert adverse drug events? A prospective eval-
uation of a commercial application. J Am Med Inform Assoc.
2008; 15:647-53.

4. Di Pentima M, Chan S, Eppes S, Klein J. Antimicrobial
prescription errors in hospitalized children: role of antimi-
crobial stewardship program in detection and intervention.
Clin Pediatr. 2009; 48:505-12.
5. Haber P, Patel M, Pan Y et al. Intussusception after rotavirus
vaccines reported to US VAERS, 2006-2012. Pediatrics. 2013;
131:1042-9.
6. Ratanajamit C, Kaewpibal P, Setthawacharavancih S,
Faroonsarng D. Effect of pharmacist participation in the
health care team on therapeutic drug monitoring uti-
lization for antiepileptic drugs. J Med Assoc Thai. 2009;
92:1500-7.
7. Hassan Y, Al-Ramahi R, Aziz N, Ghazali R. Impact of a
renal
drug dosing service on dose adjustment in hospitalized pa-
tients with chronic kidney disease. Ann Pharmacother. 2009;
43:1598-605.
8. Bhatt-Mehta V, Buck M, Chung A et al. Recommenda-
tions for meeting the pediatric patient’s need for a clinical
pharmacist: a joint opinion of the Pediatrics Practice and
Research Network of the American College of Clinical Phar-
macy and the Pediatric Pharmacy Advocacy Group. Phar-
macotherapy. 2013; 33:243-5.
Neil Patel, Pharm.D.
Pediatric Oncology
Michael Reedy, Pharm.D.
[email protected]
E. Zachary Ramsey, Pharm.D.
Pediatric Cardiology/Cardiac Intensive Care Unit

Department of Pharmacy Services
The Children’s Hospital of Philadelphia
Philadelphia, PA
The authors have declared no potential conflicts of interest.
DOI 10.2146/ajhp140887
Copyright of American Journal of Health-System Pharmacy is
the property of American
Society of Health System Pharmacists and its content may not
be copied or emailed to
multiple sites or posted to a listserv without the copyright
holder's express written permission.
However, users may print, download, or email articles for
individual use.
Transparent Electronic Health Records and Lagging Laws
Bryan S. Lee, MD, JD; Jan Walker, RN, MBA; Tom Delbanco,
MD; and Joann G. Elmore, MD, MPH
Millions of patients are accessing their medical re-cords online
via secure electronic patient portals.
They are also increasingly uploading data directly into
their records, and many clinicians now offer patients
ready and ongoing access to the notes that document
encounters. In response, patients report improved un-
derstanding of their care, better recall, enhanced ad-
herence to care plans, and an increased sense of con-
trol over their health (1).

Although these changes hold promise for improv-
ing the value and safety of health care, some opportu-
nities are hindered by legal constraints dating back to
when patients rarely saw their physical charts. To reflect
new technical and cultural realities, this legal frame-
work will require ongoing consideration and revision,
ideally reflecting strong clinician and patient input and
leadership. To highlight potential legal issues and sug-
gest strategies for active clinician involvement, we de-
scribe areas that often engender discussion and debate.
DISAGREEMENT OVER CONTENT
As patients increasingly read their medical records,
they will disagree with content, find errors, and request
changes. Online access makes poor-quality documen-
tation more apparent, particularly when notes are cop-
ied forward, templates predominate, and the patient's
story is obscured or disappears. The Health Insurance
Portability and Accountability Act (HIPAA) guarantees
patients the right to access and control distribution of
their protected health information (2). Although it per-
mits patients to request amendments, HIPAA reserves
most decision-making authority for providers, and the
general legal principle is that the provider owns the
record. However, “ownership” will become less clear as
records increasingly include information uploaded or
contributed by patients, from device data to correc-
tions or new text. The legal status of patient-derived
content will need clarification, and clinicians, patients,
and lawyers will need to join in crafting smoother pro-
cesses to resolve disputes and revise documentation.
A LITIGIOUS SOCIETY
Conceivably, clinicians sued for malpractice may

claim that plaintiffs who can review and at times con-
tribute to their online records bear increased responsi-
bility for their outcomes, including bad ones. However,
merely accessing records does not prove that the pa-
tient has reviewed them, and juries are unlikely to ex-
pect patients to understand a note, test finding, or ra-
diographic report. Conversely, malpractice plaintiffs
are often motivated by fear of a cover-up, feeling mis-
informed, or a desire to determine whether an error
occurred. Transparent access may build trust, allay
fears, and help identify important errors before adverse
consequences ensue, reducing clinicians' malpractice
liability overall (3–5).
ACCESS TO MINORS' MEDICAL RECORDS
With few exceptions, HIPAA grants parents control
of minors' medical records as their representatives and
allows them to prevent their children from accessing
online notes. However, providers can exclude parents if
they have a reasonable belief that a child is being
abused or think that parental access is not in the child's
best interest. Parents can also lose control if the minor
has a right to seek independent treatment (for exam-
ple, a condition-based exception, such as pregnancy,
or care sought by an emancipated minor). Further-
more, 14 states have “mature minor exceptions,” allow-
ing minors in some circumstances to consent to medi-
cal care without parental involvement, and 3 others
allow minors to consent regardless of age or maturity
(6). In these situations, consenting minors can control
their health information without parental permission
(7). Adding to this complexity, a 2002 federal rule al-
lows state laws that afford greater parental control to
overrule federal laws when conflicts exist.

Because of the burden of case-by-case consider-
ation and potential liability arising from disagreements,
many providers simply deny electronic access to mi-
nors and their parents. This is unfortunate, particularly
for adolescents for whom online access could be a nat-
ural way to learn about their health and how to interact
with the health care system. Providers can promote
these benefits by encouraging vendors of electronic
health records (EHRs) to develop capabilities for differ-
ential access, such as allowing adolescents but not par-
ents to view information about sexual health (8). Clini-
cians and patients could also work to convince
legislators or the U.S. Department of Health and Hu-
man Services to undo the regulation allowing state law
to preempt federal law.
MENTAL ILLNESS AND CLINICIANS' NOTES
HIPAA prevents persons from viewing their psycho-
therapy notes if they reside in a segregated part of pa-
per or EHRs. It otherwise allows access to mental health
notes, with some state laws overriding HIPAA and offer-
ing broader access that includes psychotherapy notes.
Mental health professionals are now exploring the ef-
fects of open and, at times, cogenerated online notes
as part of the therapeutic process (9). Overall, clinicians
and patients need to advocate for a more uniform land-
scape, such as a general principle of open access un-
less the clinician believes it would harm an individual
patient.
SUSPECTED ABUSE AND THE EHR
All states require clinicians to report known or sus-
pected child abuse, most require reporting elder

abuse, and some require reporting spousal abuse.
Fearing inadvertent viewing by the abuser, abuse vic-
This article was published at www.annals.org on 24 May 2016.
Annals of Internal Medicine IDEAS AND OPINIONS
© 2016 American College of Physicians 219
http://www.annals.org
tims may feel endangered by online access to records
and could decide not to confide in their clinician. Al-
though clinicians can choose to hide or not document
suspected abuse online, most current EHRs make this
inconvenient. To protect patients, clinicians and pa-
tients could advocate for laws prohibiting documenta-
tion of abuse from appearing in online portals.
SHARING NOTES AND PRIVACY
Many patients, family members, and other caregiv-
ers report benefits from sharing clinicians' notes. Much
sharing is informal, with patients reviewing their re-
cords with family members or providing passwords to
persons they trust. Unfortunately, patients face substan-
tial privacy risks from sharing passwords, and clinicians
and patients should advocate for separate, patient-
authorized “proxy access” for caregivers.
Patients could post clinicians' notes on social me-
dia and potentially threaten their reputation, particu-
larly if notes are altered or the commentary is libelous.
Currently, it is difficult for clinicians to have such mate-
rial removed, and the Communications Decency Act

prevents them from holding a Web host accountable
(10). Clinicians would benefit greatly from legal mech-
anisms that protect them from online defamation.
CLINICIAN LEADERSHIP AND NEXT STEPS
Patients' electronic access to clinicians' notes will
fundamentally change the medical record as providers
modify how they write notes and patients amend, an-
notate, and add information. Overall, we anticipate that
these changes will benefit patients and providers and
deepen the clinician–patient relationship. However,
current legal regulations will become increasingly
problematic as medical records evolve into new for-
mats and roles. Clinicians should join consumers, poli-
cymakers, regulators, informatics experts, ethicists, leg-
islators, and lawyers to help establish a legal landscape
that supports this evolution (Table).
From the University of Washington School of Medicine, Seat-
tle, Washington; Altos Eye Physicians, Los Altos, California;
and Beth Israel Deaconess Medical Center, Harvard Medical
School, Boston, Massachusetts.
Acknowledgment: James Ralston, MD, MPH, and Benjamin
W. Moulton, JD, MPH, reviewed an early version of the man-
uscript.
Grant Support: By the Robert Wood Johnson Foundation, Na-
tional Cancer Institute K05 CA 104699, and a department of
ophthalmology grant from Research to Prevent Blindness.
Disclosures: Disclosures can be viewed at www.acponline.org/
authors/icmje/ConflictOfInterestForms.do?msNum=M15-2827.

Requests for Single Reprints: Bryan S. Lee, MD, JD, Altos Eye
Physicians, 762 Altos Oaks Drive #1, Los Altos, CA 94024;
e-mail, [email protected]
Current author addresses and author contributions are avail-
able at www.annals.org.
Ann Intern Med. 2016;165:219-220. doi:10.7326/M15-2827
References
1. Delbanco T, Walker J, Bell SK, Darer JD, Elmore JG, Farag
N, et al.
Inviting patients to read their doctors' notes: a quasi-
experimental
study and a look ahead. Ann Intern Med. 2012;157:461-70.
[PMID:
23027317] doi:10.7326/0003-4819-157-7-201210020-00002
2. HIPAA, Pub. L. No. 104-191 (1996); Privacy Rule, 45 C.F.R.
§ 160,
45 C.F.R. § 164 (2002).
3. Hickson GB, Clayton EW, Githens PB, Sloan FA. Factors
that
prompted families to file medical malpractice claims following
peri-
natal injuries. JAMA. 1992;267:1359-63. [PMID: 1740858]
4. Huycke LI, Huycke MM. Characteristics of potential
plaintiffs in
malpractice litigation. Ann Intern Med. 1994;120:792-8.
[PMID:
8147552]
5. Bell SK, Folcarelli PH, Anselmo MK, Crotty BH, Flier LA,
Walker J.
Connecting patients and clinicians: the anticipated effects of
open
notes on patient safety and quality of care. Jt Comm J Qual
Patient
Saf. 2015;41:378-84. [PMID: 26215527]

6. Coleman DL, Rosoff PM. The legal authority of mature
minors to
consent to general medical treatment. Pediatrics. 2013;131:786-
93.
[PMID: 23530175] doi:10.1542/peds.2012-2470
7. Hickey K. Minors' rights in medical decision making. JONAS
Healthc Law Ethics Regul. 2007;9:100-4. [PMID: 17728582]
8. Bourgeois FC, Taylor PL, Emans SJ, Nigrin DJ, Mandl KD.
Whose
personal control? Creating private, personally controlled health
re-
cords for pediatric and adolescent patients. J Am Med Inform
Assoc.
2008;15:737-43. [PMID: 18755989] doi:10.1197/jamia.M2865
9. Kahn MW, Bell SK, Walker J, Delbanco T. A piece of my
mind.
Let's show patients their mental health records. JAMA.
2014;311:
1291-2. [PMID: 24691603] doi:10.1001/jama.2014.1824
10. 47 U.S.C. § 230(c)(1) (2015).
Table. Problem Areas and Approaches to Resolution
General
Give patients access to their full records by default
Conduct studies addressing the effects of fully transparent
medical
records (e.g., track changes in malpractice claims, capture
patient-
identified medical errors, study behaviors in adolescents with
access to records, examine the effect on patients with mental
illness)
Disagreement over medical record content
Clarify the legal status of patient-derived content

Establish mechanisms that permit transparent and parallel docu-
mentation of patient and clinician disagreement and
commentary
Develop simpler universal processes than current HIPAA
requirements to resolve disputes and revise documentation
Minors’ access to medical records
Promote separate electronic health records for teens that address
conditions justifying parental exclusion
Encourage electronic record vendors to separate and at times
shield
parts of the record involving reproductive health, thereby
facilitating appropriate shared access for teens and their parents
Work toward making electronic health records available to teens
as
the default, and adopt uniform federal policies that replace
state-by-state variation
Mental illness and clinicians’ notes
Explore legal mechanisms that offer open access to all
clinicians'
notes, including those written by mental health professionals,
unless the clinician believes access would harm an individual
patient
Suspected abuse of patients
Encourage consistency among states in documentation of
practices
that address abuse or suspected abuse
Consider federal legislation prohibiting the placement of docu-

mentation of abuse or suspected abuse on online patient portals
Shared notes and privacy
Develop universal “proxy access” mechanisms for family
members
and caregivers
Develop legal mechanisms to protect clinicians from online
defamation
IDEAS AND OPINIONS Transparent Electronic Health Records
220 Annals of Internal Medicine • Vol. 165 No. 3 • 2 August
2016 www.annals.org
http://www.acponline.org/authors/icmje/ConflictOfInterestForm
s.do?msNum=M15-2827
http://www.acponline.org/authors/icmje/ConflictOfInterestForm
s.do?msNum=M15-2827
http://www.annals.org
Current Author Addresses: Dr. Lee: Altos Eye Physicians, 762
Altos Oaks Drive #1, Los Altos, CA 94024.
Ms. Walker and Dr. Delbanco: Beth Israel Deaconess Medical
Center, Harvard Medical School, 330 Brookline Avenue, Bos-
ton, MA 02215.
Dr. Elmore: Department of Internal Medicine, University of
Washington, Harborview Medical Center, Box 359780, 325
Ninth Avenue, Seattle, WA 98104.
Author Contributions: Conception and design: B.S. Lee, J.
Walker, T. Delbanco, J.G. Elmore.
Analysis and interpretation of the data: B.S. Lee, J. Walker, T.

Delbanco.
Drafting of the article: B.S. Lee, T. Delbanco.
Critical revision of the article for important intellectual con-
tent: B.S. Lee, J. Walker, T. Delbanco, J.G. Elmore.
Final approval of the article: B.S. Lee, J. Walker, T. Delbanco,
J.G. Elmore.
Obtaining of funding: J. Walker, T. Delbanco.
Administrative, technical, or logistic support: T. Delbanco.
Collection and assembly of data: B.S. Lee, T. Delbanco.
Annals of Internal Medicine
www.annals.org Annals of Internal Medicine • Vol. 165 No. 3 •
2 August 2016
Copyright © American College of Physicians 2016.
G O V E R N M E N T , L A W , A N D P U B L IC H E A L T
H P R A C T IC E
M i s s e d P o l i c y O p p o r t u n i t i e s t o A d v a n c e
H e a l t h E q u i t y
M i s s e d P o l i c y O p p o r t u n i t i e s t o A d v a n c e
H e a l t h E q u i t y b y R e c o r d i n g
D e m o g r a p h i c D a t a in E l e c t r o n i c H e a l t h R
e c o r d s
| Megan Daugherty Douglas, JD, Daniel E. Dawes, JD, Kisha B.
Holden, PhD, MSCR, and Dominic Mack, MD, MBA

T h e s c ie n c e o f e lim in a tin g
h e a lth d is p a r it ie s is c o m p le x
and de p e n d e n t on d e m o g ra p h ic
d a ta . T h e H e a lth I n f o r m a t io n
T e c h n o lo g y f o r E c o n o m ic an d
C lin ic a l H e a lth A c t (H IT E C H )
e n c o u r a g e s t h e a d o p t i o n o f
e le c t r o n ic h e a lth r e c o r d s a n d
r e q u ir e s b a s ic d e m o g r a p h ic
d a ta c o lle c tio n ; h o w e v e r, c u r-
re n t d a ta g e n e ra te d are in s u ffi-
c ie n t to a d d re s s k n o w n h e a lth
d is p a ritie s in v u ln e ra b le p o p u -
la tio n s , in c lu d in g in d iv id u a ls
f r o m d iv e rs e ra c ia l a n d e th n ic
b a c k g ro u n d s , w it h d is a b ilitie s ,
a n d w it h d iv e rs e s e x u a l id e n ti-
ties.
W e c o n d u c te d an a d m in is -
tr a tiv e h is to ry o f HITECH an d
id e n tifie d g a p s b e tw e e n th e
p o lic y o b je c tiv e a n d re q u ire d
m e a s u re . W e id e n tifie d 20 o p -
p o r tu n itie s f o r c h a n g e a n d 5
c h a n g e s , 2 o f w h ic h re q u ire d
th e c o lle c tio n o f less d a ta .
U ntil health care d e m o g ra p h ic
data co lle c tio n re q u ire m e n ts are
co n s is te n t w ith p u b lic health re-
q u ire m e n ts , th e n a tio n a l goal o f
e lim in a tin g health d isp arities
ca n n o t be realized. (Am J Public
Health. 2 0 1 5 ;1 0 5 :S 3 8 0 - S 3 8 8 .
d o i:1 0 .2 1 0 5 /A J PH.2014.302384)

FEDERAL EFFORTS TO
address racial and ethnic health
disparities were initiated by the
Heckler Report in 198 5.1 Nearly 3
decades later, health disparities
persist across racial and ethnic
groups and have been estimated to
cost $ 3 0 0 billion per year.2 De-
mographic data, the statistical data
of a population, is the foundation
for identifying disparities, improv-
ing overall quality of health care,
improving population health, and
measuring progress toward health
equity.3 Accurately recording de-
mographic data enables health care
providers to identify risk and pro-
tective factors for a large num ber
of diseases and conditions and to
improve comprehensive care for
individual patients.
As understanding of health dis-
parities and contributing risk fac-
tors improves, the need for more
granular information has in-
creased.3 Racial and ethnic mi-
nority populations continue to in-
crease, resulting in cultural and
linguistic issues that have an im-
pact on delivery of care and
treatment. People with disabilities
make up 2 0 % of the adult popu-
lation and are burdened by pre-
ventable disparities in health care

compared with their nondisabled
peers.4 Lesbian, gay, bisexual, and
transgender individuals are be-
coming increasingly visible in our
society and have worse outcomes
for a num ber of medical condi-
tions than their heterosexual and
cisgender (individuals identifying
as their birth sex) peers.5
In 1997, the Office of Manage-
m ent and Budget (OMB) revised the
government-unique race and eth-
nicity standards to include 5 race
and 2 ethnicity categories (Table
l).6 Recognition of the diversity
within each OMB race and ethnicity
category is critical to eliminating
health disparities.3 For example,
among Asians in California, rates of
colorectal screening varied across
racial subgroups, with disparities
seen in Chinese, Korean, and Viet-
namese individuals compared with
Whites, but no disparity seen in
other Asian subgroups.7 In this in-
stance, the intervention most effec-
tive in reducing the disparity would
target Chinese, Korean, and Viet-
namese patients, rather than all
Asian individuals. For this reason,
recent health disparity reports con-
sistently call for the collection of
more detailed and consistent infor-
mation across the health care and

public health systems.7' 9 Under the
Affordable Care Act (ACA), the
Department of Health and Human
Services developed more granular
race and ethnicity standards and
added 6 functional questions to
assess disability status (Table l).10
THE HITECH ACT
In 2 0 0 9 , Congress passed the
Health Information Technology for
Economic and Clinical Health Act
(HITECH) and invested more than
$35 billion to stimulate the adop-
tion and meaningful use of elec-
tronic health records (EHRs) by
physicians and hospitals.11 One of
the primary goals of HITECH was
to reduce health disparities.11 As
proof of the law’s reach, by 2013,
69% of physicians intended to or
were already participating in the
Medicare or Medicaid EHR incen-
tive program.12 Physician EHR
adoption increased from 25% in
2010 to 4 0 % in 2012 and hospital
adoption rates nearly tripled to
4 4 % during the same time period.13
T he HITECH programs have
evolved through a staged rule-
making process, resulting in
a dense, complex, and convoluted
administrative history. No compre-

hensive look at HITECH’s admin
istrative process with regard to
demographic data collection
currently exists. Therefore, this
study provides much-needed doc-
umentation of the rulemaking
S 3 8 0 | Government, Law, and Public Health Practice | Peer
Reviewed | Douglas e t al. American Journal o f Public Health |
S upplem ent 3 , 2 0 1 5 , Vol 1 0 5 , No. S3
G O V E R N M E N T , LA W , A N D P U B L IC HEALTH P
R A C TIC E
TABLE 1-C o m p ariso n of Race and Ethnicity Collection
Standards Adopted by the Office of M anagem ent
and Budget in 1 9 9 7 and the D epartm ent of Health and
Human Services in 2 0 1 1
Office o f Management and Budget6 Department of Health and
Human Services10
Demographic (Last Revised in 1997) (Adopted in 2011)
Black or African American Black or African American
American Indian or Alaska Native American Indian or Alaska
Native
Asian Asian Indian
Chinese
Filipino

Japanese
Korean
Vietnamese
Other Asian
Native Hawaiian or other Pacific Islander Native Hawaiian
Guamanian or Chamorro
Samoan
Other Pacific Islander
White White
Non-Hispanic or Latino Non-Hispanic/Latino/Spanish origin
Hispanic or Latino Mexican
Cuban
Puerto Rican
Other Hispanic/Latino/S panish origin
process related to recording de-
mographic data.
O ur specific aims w ere (1) to
construct a comprehensive ad-
ministrative history of HITECH
with regard to recording demo-
graphic data, (2) to determ ine the
num ber of opportunities for policy
change and policy changes that
arose throughout the process, and
(3) to identify the reasons for

adopting o r declining opportuni-
ties for policy change with regard
to recording demographic data.
T he primary purpose of this
analysis was to support the col-
lection of enhanced demographic
data across various health sectors.
It is our intention to unite health
care providers, public health
practitioners, consumers, EHR
vendors, advocates, and policy-
makers in an effort to develop and
adopt robust, forward-thinking
policies on the collection of de-
mographic data in EITRs that will
lead to the reduction and ultimate
elimination of health disparities.
METHODS
W e compiled the HITECH ad-
ministrative history by using the
Federal Register’s online advanced
search tool. We identified all ad-
ministrative actions taken between
February 17, 2009, and February
28, 2014, by using the search term
“HITECH." We collected and
reviewed for relevancy every article
with the search term “demographic”
W e excluded articles related to
privacy and security, health care
payment and delivery systems, and

specific data collection notices.
W e limited our demographic
categories of interest to granular
race and ethnicity data, preferred
language, disability status, sexual
orientation, and gender identify.
W e conducted a targeted search of
each relevant document by using
the following key terms: disparit*,
demographic, race, ethnicity, lan-
guage, disabilit*, and sexual. Where
these terms appeared, we collected
the entire section related to the term
and additional information neces-
sary for contextual understanding.
W e defined and applied vari-
ables to the relevant sections of
each article. “Baseline" was the
statutory minimum or final rule
Supplem ent 3 , 2 0 1 5 , Vol 1 0 5 , No. S3 | Am erican Journal
o f Public Health Douglas et al. | Peer Reviewed | Government,
from the previous action. W e de-
fined “proposed category” as the
categories of demographic data
proposed for collection. W e de-
fined “final category” as the cate
gories adopted in the final rule.
“Standard” was the common ter
minology used to support each
dem ographic d ata category.
“O pportunity for change” was
the explicit consideration by the

agency o f m ultiple categories or
standards. “Change” was a change
in category o r standard from the
baseline to the final rule (Table 2).
From these findings, we con-
structed a timeline of every HITECH
administrative action relevant to re-
cording demographic data (Figure 1).
W e included actions taken in
accordance with the ACA’s demo
graphic data collection standards to
allow for temporal comparison.
RESULTS
The administrative history
search of the Federal Register
resulted in 136 articles. Once we
applied the exclusion criteria, 9
regulatory actions rem ained rele-
vant. W e identified 2 HITECH
programs: (1) the Medicare and
Medicaid EHR Incentive program
(the Meaningful Use program
[MU]), administered by the Cen-
ters for Medicare and Medicaid
Services (CMS) and (2) the Health
Information Technology (HIT)
Standards and Certification Crite-
ria program (SCC), administered
by the Office of the National Co-
ordinator (ONC). Five of the reg-
ulatory actions w ere proposed or
interim final rules, 2 for the MU
program (stages 1 and 2) and 3 for

the SCC program (initial, 2 0 1 4
edition, and 2 0 1 5 voluntary
Law, and Public Health Practice S 381
TA
BL
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2
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e
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ic
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at

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ts
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S382 | Government, Law, and Public Health Practice | Peer
Reviewed | Douglas et al. American Journal of Public Health |
Supplement 3, 2015, Vol 105, No. S3
Ot
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Supplement 3, 2015, Vol 105, No. S3 | American Journal of
Public Health Douglas et al. | Peer Reviewed | Government,
Law, and Public Health Practice S383
G O V E R N M E N T , L A W , A N D P U B L IC H E A L T
H P R A C T IC E
o ★ ☆
Jan
2 0 1 0
Aug/Sept
2010
June
2011
M u Stage 1
Initial Standards and C ertification
! O c t March Sept/O ct
2011
_____________ I

2012
I
2012
I
ACA M u Stage 2
Standards on 2014 Edition
d em ographic Standards and C ertification
data collection
2015 Vo luntary Edition
Standards and C ertification
FIG U R E 1 —T im e lin e o f a d m in is tra tiv e a c tio n s
u n d e r th e H e a lth In fo rm a tio n Technology fo r
Econom ic and
C lin ic a l H e a lth A c t (H IT E C H ) an d th e A ffo rd a b
le C a re A c t (A C A ): U n ite d S ta te s , 2 0 1 0 - 2 0 1 4 .
Symbols:
Key
o HITECH Act
☆ A ffo rda ble Care Act
Sym bol colors:
0 3 Proposed ru le /
In te rim Final rule
□ Final rule

N o te . MU - the Meaningful Use program.
edition). Four were final rules, 2
for the MU program and 2 for
the SCC program. In total, there
were 2 0 opportunities for policy
change. Five changes were made,
with 2 of those changes eliminat-
ing a category of demographic
data, and a num ber of opportuni-
ties rem ain to be determined. T a-
ble 2 shows all opportunities for
change and all actual changes.
Round 1
T he administrative actions for
stage 1 of the MU program and the
initial SCC for certified EHRs co-
incided, with the proposed rules
published in the Federal Register
on January 1 3 ,2 0 1 0 , and the final
rules becoming effective on Sep-
tem ber 27, 2 0 1 0 , and August 27,
2 0 1 0 , respectively.
Meaningful Use, stage 1. In the
MU proposed rule,14 the recording of
demographic data was proposed as
a core (required) objective. Within
the objective, the proposed cate-
gories were race, ethnicity, gender,
date of birth, preferred language,
and insurance type. The OMB stan-
dards were proposed for race and
ethnicity. No standards were pro-

posed for preferred language.
From the proposed to the final
rule,15 there were 3 opportunities for
policy change and 1 change: insur-
ance type was eliminated from the
requirements (Table 2). Comments
on the complexity of defining insur-
ance type and attributing it to pa-
tients in a consistent way merited its
elimination as a core measure. Citing
the Institute of Medicine report en-
titled “Race, Ethnicity and Language
Data: Standardization for Health
Care Quality Improvement,” com-
menters recommended more gran-
ular racial and ethnic standards that
roll up to the 5 OMB standards;
however, the minimal OMB stan-
dards were adopted in the final rule.
The agency reasoned that expanding
the OMB categories was ‘beyond the
scope of the definition of meaningful
use to provide additional definitions
for race and ethnicity... .”15
Initial set o f Standards and
Certification Criteria. T he SCC
rulemaking was consistent with
the MU rulemaking with regard to
recording demographic data.16'1'
Commenters recom m ended addi-
tional categories of demographic
data, including birthplace, educa-
tion, occupation o r industry, and

functional status. Because the
agency did not address each cate-
gory separately, all of these rec-
ommendations were counted as
a single opportunity for policy
change. In total, there were 4
opportunities for policy change
and 1 change: insurance type was
eliminated from the requirem ents
(Table 2). T he SCC final rule
established the OMB standards for
race and ethnicity.
Round 2
T he adm inistrative actions for
stage 2 of th e MU program and
th e 2 0 1 4 edition SCC for certi-
fied EHRs occurred sim ulta-
neously, w ith the proposed rules
published in th e Federal Register
on M arch 7, 2 0 1 2 , and the final
rules becom ing effective on Sep-
tem ber 4, 2 0 1 2 , and O ctober 4,
2 0 1 2 , respectively.
Meaningful Use, stage 2. From
the proposed to the final rule, there
was a total of 7 opportunities for
change and 1 actual change (Table
2) 18,19 y jje OMB standards for race
were recommended in the pro-
posed rule, and voluntary recording
of additional categories was

encouraged if they m apped to
the 5 OMB categories. T h e CMS
requested comments on the collec-
tion of disability status, highlighting
the benefits to care coordination
from gathering this information in
the EFfR. The CMS also sought
comment on whether sexual orien-
tation and gender identity should be
recorded in EHRs.
In the final rule, CMS reported
several comments recommending
alternative race and ethnicity stan-
dards, specifically the Centers for
Disease Control and Prevention and
the US Census Bureau standards.
The agency declined to change but
Reviewed | Douglas e t al. Am erican Journal o f Public Health |
S upplem ent 3, 2 0 1 5 , Vol 1 0 5 , No. S3
encouraged the voluntary collection
of more granular data mapping to
the OMB categories. The CMS
adopted the term “sex” to replace
“gender” on the basis of comments
clarifying that “gender” is a soda!
construct and “sex” is a physiologi
cal characteristic at birth.
Many commenters supported the
addition of disability status, sexual

orientation, and gender identity. Yet
some comments questioned the
clinical significance of recording this
information as demographic data
The CMS declined to adopt disabil-
ity status or sexual orientation and
gender identify because of the lack
of consensus on definitions, lack of
agreed-upon standards, data collec-
tion and reporting challenges, and
disagreement over where and how
to collect this information in an
EHR.
Standards and Certification
Criteria, 2 0 1 4 edition. From the
proposed rule to the final rule,
there was a total of 6 opportunities
for change and 2 actual changes
(Table 2).20'21 T he ONC proposed
to maintain the OMB race and
ethnicity categories. The ONC
proposed to adopt the Interna-
tional Organization for Standardi-
zation’s (ISO’s) language standard
ISO 639-1 as the preferred lan-
guage vocabulary standard as op-
posed to the more granular ISO
639-2 standard.22 T he ONC
requested comments about incor-
porating disability status into de-
mographic data, citing the many
benefits of making this change,
from improving access, coordinat-
ing care across multiple providers,
and monitoring disparities be-

tween “disabled” and “nondis-
abled” populations. T he ONC did
not seek comments on w hether
S upplem ent 3, 2 0 1 5 , Vol 1 0 5 , No. S3
sexual orientation and gender
identity data should be collected.
T he final SCC rule clarified the
preferred language standards
based on the comments received,
and ISO 639-2 constrained by
639-1 was adopted because con-
straining ISO 639-2 to only the
active languages in 639-1 would
permit more granularity and is
a better approach than in the pro-
posed rule.22 Commenters sug-
gested 3 alternative race and
ethnicity standards based on the
Institute of Medicine recommen-
dations, the Centers for Disease
Control and Prevention vocabulary
standards, and those adopted by
the Department of Health and
Human Services to comply with
the ACA, all of which are more
granular than the OMB standards.
T he final rule declined this change,
reasoning that the OMB categories
are a government-unique standard,
are easily understood, and are
readily available making them the
best standards to support the policy
goals. The agency stated that EHR

technology must have the capabil-
ity to map race and ethnicity to the
OMB categories if the technology
developer chooses to incorporate
more granular race and ethnicity
categories. Disability status was not
adopted for reasons similar to
those of CMS. Commenters rec-
ommended the incorporation of
identity, but the agency declined to
make this change.
R o u n d 3
On February 26, 2 0 1 4 , the
ONC released a notice of proposed
rulemaking for the voluntary
2 0 1 5 edition EHR certification
criteria (2015 SCC), which lacked
a CMS Meaningful Use program
counterpart.23 T he proposed rule
anticipated a MU stage 3 proposal
in the fall (available as a supple-
m ent to the online version of this
article at http://www.ajph.org).
T he proposed rule identified
challenges based on the previous
action (SCC 2 0 1 4 edition final
rule) adopting preferred language
standards. Since the final rule’s
publication, ONC published a list
of frequently asked questions to
clarify the standards and ac-

knowledged that the approach
taken in the final rule failed to
support current languages, includ-
ing sign language and Hmong.24
Because of this oversight, the
2 0 1 5 SCC proposed rule sought
comment on 3 options: full adop-
tion of ISO 639-2 codes, adoption
of ISO 639-3 codes, or adoption of
standards included in “Tags for
identifying languages, September
2 0 0 9 ,” a memo describing current
best practices for language identi-
fication.22 (ISO 639-1 consists of
2-letter codes representing most of
the major languages of the world.
ISO 63 9 -2 consists of 3-letter
codes representing m ore lan-
guages than ISO 639-1. ISO
639-3 consists of 3-letter codes
and is the most comprehensive of
the ISO series, including living,
extinct, and ancient languages.)
Following the proposed rule,
the ONC sought comments on
changes to the SCC in anticipation
of the 2 0 1 7 edition. Up for con-
sideration were the recording of
disability status, sexual orienta-
tion, gender identity, military sta-
tus, and industry or occupation.
Comments were sought on the
appropriateness of these cate-
gories and ways to include them in

current demographic data re-
quirements. T he rule proposed 6
functional questions currently in-
cluded in the American Commu-
nity Survey with the addition of
a question about English profi-
ciency, seeking comment on
w hether the questions were ap-
propriate or if better alternatives
exist and how to capture this in-
formation in an EHR. Sexual ori-
entation and gender identity stan-
dards were proposed on the basis
of the recent IOM report, “Col
lecting sexual orientation and
gender identity data in electronic
health records: workshop sum-
mary.” Comments on the collec
tion of military service history and
occupation and industry were
requested. T he comment period
for this proposed rule closed on
April 28, 2 0 1 4 .
D I S C U S S I O N
T here is a gap between the
criteria and standards supporting
the MU measure recording demo-
graphic data and the policy objec-
tive of reducing health disparities.
Medical practices are driven by the
MU criteria and, without require-
ments for more informative data,
providers are not encouraged
through the policy to identify per-

tinent demographics that lead to
proper clinical diagnosis and im-
proved outcomes. Evidence-based
measures that better support the
policy objective exist and are in-
cluded in public health programs
and surveys (Table 3).
T he inconsistent demographic
data collection standards between
the HITECH programs and the ACA
programs may exacerbate health
disparities and are problematic for
Am erican Journal o f Public Health Douglas e t at. | Peer
Reviewed | Government, Law, and Public Health Practice | S 3 8
5
http://www.ajph.org
both research and practice. Practice
is hindered because public health is
collecting information that, in the
case of disability status, sexual ori-
entation, and gender identity, has
limited clinical comparison, and with
regard to race and ethnicity, is more
informative than the data being col-
lected in EHRs. Research using
public health survey data will pro-
vide specific information that cannot
be ad ap ted to the clinical level
because of insufficient d ata col-
lection in EHRs. T h e ONC and
CMS recognize the importance of

comparable data between EHRs and
public health, yet this study shows
the agencies have declined nearly
eveiy opportunity to align the De-
partment of Health and Human
Services data adopted in the ACA
with the MU and SCC programs.16
Although ONC and CMS have
declined to require expanded de-
mographic data collection, the
agencies encourage providers to
voluntarily collect additional demo-
graphic data as is appropriate for
their practice.16 This suggestion is
merely an illusion of flexibility and
expanded data collection efforts as
most EHR vendors are solely fo-
cused on building systems compliant
with the SCC criteria (Andy Slavitt,
chief executive officer, Optumlnsight,
stated to the Subcommittee on
Healthcare and Technology Sub-
committee on Small Business “[N]ew
product development is focused on
satisfying those regulatory hurdles,
rather than on simple innovations
that improve productivity.”25)
Therefore, health care providers
who wish to collect more informa-
tion must expand their budgets and
payment structures to develop the
functionality and infrastructure

within their individual EHR system
or build the capacity in their own
information technology depart-
ments. This is particularly challeng-
ing for health care providers that
serve minority and underserved
communities who are less likely to
have the financial means to build
this capacity. Until expanded demo-
graphic data categories are included
in the SCC program requirements,
vendors lack incentives to build the
capacity within their EHRs.
T A B L E 3 - P o l i c y G a p s B e t w e e n D e m o g r a p
h i c D a t a R e q u i r e m e n t s P r o p o s e d a n d A d o
p t e d in t h e M e a n i n g f u l U s e P r o g r a m a n d T
h o s e U s e d in
P u b l i c H e a l t h S u r v e y s
D e m o g ra p h ic
D a t a C ateg o ry
P o s s ib le E v id e n c e -B a s e d
S t a n d a r d s (E x p lic itly
A c k n o w le d g e d in F in al
R ules ) N o . o f C a te g o rie s P ro p o s e d in M U A d o p
te d in M U U se d in P u b lic H e a lt h Surveys
R a c e 0 M B 5 X X X
D H H S 1 4 " X X
CDC V cn CD O X X
I0 M L o c a lly re le v a n t c h o ic e s " X NA

E th n ic ity 0 M B 2 X X X
D H H S 5 " X X
CDC > 3 0 " X X
I0 M L o c a lly re le v a n t c h o ic e s X NA
P re fe rre d la n g u a g e IS O 6 3 9 - 1 > 2 0 0 X Xb X
IS O 6 3 9 - 2 > 5 0 0 X xb X
IS O 6 3 9 - 3 A p p r o x im a te ly 6 0 0 0 X X
Tags f o r Id e n tify in g D e v e lo p s u n iq u e id e n tifie
rs X NA NA
L a n g u a g e s , S e p te m b e r f o r la n g u a g e s in c lu d
e d in
2 0 0 9 IS O 6 3 9 registry
Sex 2 X X X
D is a b ility o r fu n c tio n a l A m e r ic a n C o m m u n ity
S u rv ey 6 X X
s ta tu s
S e x u a l o r ie n ta tio n H L 7 8 X X
G e n d e r id e n tity H L 7 8 X
N ote. CDC = C e n te rs f o r D is e a s e C o n tro l a n d P
r e v e n tio n ; D H H S = D e p a r t m e n t o f H e a lt h a n
d H u m a n S e rv ic e s ; H L 7 = H e a lt h Level S e v e n
In t e r n a tio n a l; I 0 M - In s tit u t e o f M e d ic in e ;
IS O - In t e r n a tio n a l
O rg a n iz a tio n f o r S t a n d a r d iz a t io n ; M U - t h e
M e a n in g f u l U se p ro g ra m ; NA = n o t a p p lic a b le ;
0 M B - O ffic e o f M a n a g e m e n t a n d B u d g e t.
"A ll s u b c a te g o r ie s ro ll u p t o 0 M B c a te g o rie s .
bI S 0 6 3 9 - 2 a lp h a - 3 c o d e s lim ite d t o t h o s e t
h a t a ls o h a v e a c o r re s p o n d in g a lp h a - 2 c o d e
in IS O 6 3 9 - 1 .

S 3 8 6 | G o v e r n m e n t , L a w , a n d P u b l i c H e a l t
h P r a c t i c e | P e e r R e v i e w e d | D o u g l a s e t a l .
A m e r i c a n J o u r n a l o f P u b l i c H e a l t h | S u p p
l e m e n t 3 , 2 0 1 5 , V o l 1 0 5 , N o . S 3
G O V E R N M E N T, LAW , A N D P U B L IC HEALTH P
R A C TIC E
It is difficult to gauge th e like-
lihood for policy change in the
MU and SCC programs, b u t the
2 0 1 5 voluntary SCC proposed
rule may provide som e insight
into future rulemakings. It is thus
far the m ost aggressive proposal
w ith regard to adding categories
of dem ographic data; however, it
proposed to m aintain the mini-
mally informative OMB standards
for race and ethnicity. T he evo-
lution of the preferred language
standards is a prom ising prece-
dent, although the challenges ex-
perienced with adopting a single
standard may d eter future ag-
gressive policies.
Limitations
T he methodology used in this
study was time-consuming, but it
comprehensively collected all ad-
ministrative actions taken within

the timeframe of interest. This
study did not look at the HITECH
legislative history or the recom-
mendations of the subagency HIT
Policy Committee or the HIT
Standards Committee, which
would provide even m ore insight
into the policymaking process.
These methods do not include
uses of demographic data in EHRs
beyond the MU core objective of
“record demographics.” Other MU
objectives utilize similar informa-
tion. For example, functional sta-
tus was adopted in MU stage 2 as
a requirem ent for the care sum -
mary document. However, limit-
ing these data to the care summary
docum ent maintains the long-held
view o f disability as m erely a
m edical condition and precludes
analysis o f prev en tab le health
disparities th a t have an im pact
on people w ith disabilities.
Conclusions
T he use of EHRs to identify and
reduce health disparities is prom-
ising, but limited by the type of
demographic data that is currently
collected. To recognize HITECH’s
policy priority of reducing health
disparities, more granular race and
ethnicity d a ta disability status, and

identity must be collected in EEIRs.
The only way to ensure the con-
sistent and comprehensive collec-
tion of this information is to in-
corporate expanded requirements
into the MU and SCC programs.
Public health leaders have a re-
sponsibility to encourage health
care providers, EHR vendors, and
policymakers to adopt and effec-
tively implement evidence-based
policies and practices necessary to
help document and eliminate
health disparities. ■
A bo ut th e A uthors
Megan D. Douglas and Dominic Mack are
with the National Center f o r Primary Care,
Morehouse School o f Medicine, Atlanta,
GA. Daniel E. Dawes is with the Office o f
the President, Morehouse School o f Medi-
cine. Kisha B. Holden is with the Satcher
Health Leadership Institute, Morehouse
School o f Medicine.
Correspondence should be sent to Megan
Daugherty Douglas, National Center fo r
Primary Care, 7 2 0 Westview Dr, NCPC
Bldg, Ste 3 0 0 , Atlanta, GA 3 0 3 1 0
(e-mail: [email protected]). Reprints
can be ordered at http://www.ajph.org by
clicking the ‘‘Reprints’’ link.
This article was accepted October 4,
2 0 1 4 .

C on trib u to rs
M. D. Douglas was project director for
this study and responsible for m ethodol-
ogy developm ent, analysis, an d writing.
K. B. Holden contributed to the writing
and editing. D. Mack was the principal
investigator o f this project and along with
D. E. Dawes conceptualized th e study
and contributed to the writing.
Acknow ledgm ents
T h e p ro je c t d esc rib e d w as su p p o rte d
by th e N ational In stitu te on M inority
H ealth an d H ealth D isparities g ra n t
U 5 4 M D 0 0 8 1 7 3 , a c o m p o n e n t o f th e
N ational In stitu te s o f H ealth.
Note. T he article’s contents are solely
the responsibility o f the authors and do
not necessarily represent the official views
of th e National Institute on Minority
H ealth and H ealth Disparities o r the
National Institutes of Health.
Hum an P a rtic ip a n t P ro tectio n
No protocol approval was necessary b e -
cause all data w ere obtained from pub-
licly available secondary sources.
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http://csmh.umaryland.edu/Toolbar/
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R A C TIC E
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R e v ie w o f S t a t e L e g is la t iv e A p p r o a c h e s t o
E lim in a t in g R a c ia l
a n d E th n ic H e a lt h D is p a r i t i e s , 2 0 0 2 - 2 0 1 1

| Jessica L. Young, PhD, MS, Keshia Pollack, PhD, MPH, and
Lainie Rutkow, JD, PhD, MPH
W e co n d u cte d a legal m a p -
p in g s tu d y o f state b ills related
to ra c ia l/e th n ic h e a lth d is p a r-
itie s in a ll 50 s ta te s b e tw e e n
2002 a n d 2011.
Forty-five states introduced
at least 1 bill th a t specifically
targeted racial/ethnic health dis-
parities; w e analyzed 607 total
bills. O f these 607 bills, 330 w ere
passed into law (54.4%). These
b ills a p p ro a c h e d e lim in a tin g
racial/ethnic health disparities by
developing governm ental infra-
structure, p roviding appropria-
tions, and focusing on specific
diseases and data collection. In
addition, states tackled em erg-
ing topics that w ere previously
lacking laws, particularly His-
panic health.
Legislation is an im p o rta n t
p o licy to o l fo r states to advance
th e e lim in a tio n o f racial/ethnic
health disparities. [Am J Public
Health. 2 0 1 5 ;1 0 5 :S 3 8 8 -S 3 9 4 .
doi:10.2105/AJPH.2015.302590)
DESPITE DECADES OF
re se a rc h a n d aw areness,1-3 a n d
in c re a s in g fe d e ra l a tte n tio n a n d

a ctio n ,4-7 r a c ia l/e th n ic h e a lth
d isp a ritie s p e rs is t th r o u g h o u t US
society. It is w ell d o c u m e n te d
th a t so m e r a c ia l/e th n ic g ro u p s
a re m o re likely to live s h o r te r
a n d sic k e r lives.8-10 H e a lth d is-
p a ritie s also v a ry geo g rap h ica lly .
F o r e xam ple, r e s e a rc h su g g ests
th a t th e r e a re m o re se v e re
ra c ia l/e th n ic h e a lth d isp a ritie s
a m o n g ru r a l p o p u la tio n s com -
p a r e d w ith u r b a n d w e llin g p o p -
u la tio n s.11 T h e s e h e a lth d is p a r-
ities a re th e re s u lt o f m y ria d
social, in d iv id u a l, a n d p o litical
factors, in c lu d in g h e a lth b e h a v -
iors, h o u sin g , e d u c a tio n , incom e,
a n d access to h e a lth c a re .12-15
B e ca u se o f th e co m p le x n a tu r e of
th e d riv e rs o f h e a lth d isp a rities,
e lim in a tin g r a c ia l/e th n ic h e a lth
d isp a ritie s re q u ir e s in te g ra tin g
science, p ra c tic e , a n d policy a t all
levels o f g o v e rn m e n t.16
States are well positioned to use
th eir policymaking pow ers tow ard
eliminating ra cial/ ethnic health
disparities, a n d h ave d o n e so in the
past.17 State legislative activities re -
lated to racial/ethnic health dispar-
ities have focused on developing
governm ental infrastructure
focused on racial/ethnic health dis-
parities, disease-specific approaches

(e.g., lupus task forces), race-specific
activities (e.g., African A m erican oral
health programs), and increasing
awareness of health disparities
through special commissions.1'
Few researchers h a v e devoted
attention to m apping state legisla-
tive activity regarding racial/ethnic
h ealth disparities. By n o t doing so,
w e miss opportunities to further
o u r u n d erstanding o f ho w states
h a v e u sed legislation to elim inate
ra cial/ethnic h e alth disparities, and
to su p p o rt advocacy and m onitor-
ing efforts related to racial/ethnic
health disparities. T o o u r know l-
edge, L adenheim and G rom an
published th e first study in this
area, by review ing state legislation
th at specifically targeted racial/
ethnic disparities in health care a n d
access from 1 9 7 5 to 2 0 0 1 .17 W e
furthered th e und e rstan d in g o f the
re c e n t state legislative environm ent
related to elim inating ra cial/ethnic
h ealth disparities. O u r analysis ex-
a m in e d p r o p o s e d a n d enacted
state legislation from 2 0 0 2 to 2011
to identify legislative a p p ro a c h e s
to elim in a tin g ra c ia l/e th n ic health
disparities. O ur research, which
considered state bills that w ere pro-
posed and failed along with those
that were passed into law, offered

insights into states’ legislative agendas
related to health disparities, including
emerging trends and challenges.
METHODS
W e co n d u cted a legal m apping
stu d y o f p ro p o se d a n d e n acted
legislation re la te d to ra c ia l/e th n ic
h e a lth disparities in all 5 0 states
b e tw e e n 2 0 0 2 a n d 2 0 1 1.18 W e
e x am ined state-level bills th a t
w e re in tro d u c e d a n d failed, a nd
those th a t w e re in tro d u c e d and
ultim ately b e ca m e law.
Data Collection
W e u sed a systematic and struc-
tu red keyw ord search o f introduced
Reviewed | Y oung e t at. American Journal o f Public Health | S
upplem ent 3 , 2 0 1 5 , Vol 1 0 5 , No. S3
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Q U A L I T Y I M P R O V E M E N T R E P O R T
Improving documentation of quality measures in the electronic
health record
Peg Esper, DNP, MSN, MSA, ANP-BC, AOCN (Nurse
Practitioner)1 & Suzette Walker, DNP, MSN, FNP-C, AOCNP
(Nurse Practitioner)2
1 University of Michigan, Ann Arbor, Michigan
2 McKenzie Health System, Sandusky, Michigan
Keywords
Quality improvement; nurse practitioners;
oncology; information technology; palliative
care.
Correspondence
Peg Esper, DNP, MSN, ANP-BC, AOCN,
University of Michigan, Ann Arbor, MI.
E-mail: [email protected]
Received: 22 April 2013;
accepted: 25 August 2013
doi: 10.1002/2327-6924.12169
Disclosures

Preliminary study findings were presented via
poster at ONS Connections Conference,
Phoenix, AZ—November, 2012 and ASCO
Quality Conference, San Diego, CA—December,
2012.
Abstract
Purpose: Oncology quality measures provide an important tool
to evaluate care
received by cancer patients. These measures are frequently
addressed by oncol-
ogy nurse practitioners (NPs). NP documentation of quality
oncology practice
initiative (QOPI) measures in the electronic health record
(EHR) is evaluated in
this study.
Data sources: NP documentation of specific QOPI measures
before and after
an educational intervention (EI) was evaluated. EHR shortcuts,
called “Smart-
Phrases,” were used to increase efficiency in documentation of
these measures.
Conclusions: Preintervention chart audits found compliance
<80% in the mul-
tiple measurement areas. Following the EI, NPs surveyed
identified greater un-
derstanding of QOPI measures and an interest in using
“SmartPhrases” to aid in
measure documentation. The postintervention audit
demonstrated improvement
in all areas addressed during the EI noting the use of

“SmartPhrases” based on
descriptive findings.
Implications for practice: NPs play a significant role in
providing quality care
for oncology patients. By increasing knowledge related to the
documentation of
quality measures and providing tools to increase the efficiency
associated with
their documentation, a positive impact can be made in efforts to
promote quality
patient care.
Purpose
Introduction
The provision of quality care is an expectation for oncol-
ogy practitioners throughout the patient’s illness contin-
uum. Improved treatments have significantly lengthened
this trajectory and have led to increased survival for many
patients with cancer. A new dimension in care, identified
as supportive care, focuses on improving the quality of pa-
tients’ lives during and after treatment. Supportive care
includes both palliative care (care that seeks to decrease
suffering at all disease stages) and symptom management.
Although a third of patients die within 5 years of a
cancer diagnosis, patients with cancer are living longer
(American Cancer Society, 2012). This has resulted in
patients having more long-term side effects than ever
before. In addition, the increase in treatment options
and life expectancy has exposed patients to side ef-
fects not previously seen in this population. This has re-
sulted in an increased demand for quality symptom man-
agement for all patients. A landmark randomized trial

published by Temel et al. (2010) documented that pa-
tients who receive palliative care throughout the course
of illness lived longer and reported improved quality of
life.
Quality measures in oncology
In 2002, in an attempt to improve the quality of care
for all oncology patients, the American Society of Clin-
ical Oncology (ASCO) developed the quality oncology
practice initiative (QOPI). Although supportive care mea-
sures were part of the initial measurement set, they
were greatly expanded on over the next few years. The
QOPI measures were piloted and published by McNiff
et al. (2008). These measures represent the only national,
systematic, practice-based quality initiative that allows on-
cology practices to capture data related to supportive care.
308 Journal of the American Association of Nurse Practitioners
27 (2015) 308–312
C©2014 American Association of Nurse Practitioners
P. Esper & S. Walker Improving documentation of quality
measures
Nurse practitioners (NPs) have a critical role in symptom
management and are key providers in assuring incorpora-
tion of these quality measures.
Electronic health records and quality measurement
With the governmental impetus to incorporate evidence
of “Meaningful Use” for healthcare system reimburse-
ment, more and more organizations are either moving to

or upgrading their electronic health record (EHR). Sig-
nificant interest lies in the ability to use the EHR to
capture documentation of a variety of measures, includ-
ing quality measures. To date, several studies have eval-
uated this with mixed responses. In a study involving
primary care clinics by Linder, Kaleba, and Kmetik (2009),
EHR encounters for 688 patients with a claim diagnosis
of pneumonia, were reviewed. Wide variation in perfor-
mance measurement was noted and accurate identifica-
tion of quality measures in the EHR was noted to be chal-
lenging. Another interesting finding was noted in a retro-
spective study by Parsons, McCullough, Wang, and Shih
(2012), in which over 4000 records across 57 practices
were reviewed to determine the validity of EHR-derived
quality measures following a comprehensive training pro-
gram. Their findings showed that the EHR-derived report-
ing could have a disproportionately negative impact on the
ability to capture this information based on workflows and
other payor-based requirements for documentation.
Persell et al. (2011), however, did find in a large time
series that EHR tools could be used to accelerate improve-
ment in performance of quality measures based on a qual-
ity improvement intervention that included clinician feed-
back. The quality improvement study addressed inefficien-
cies in the EHR and included a mechanism to inform clin-
icians when quality measures were not being met (such
as important medications not being received by patients).
Overall, there remains a paucity in the literature related
to the relationship between staff education and improving
documentation of quality measures via an EHR.
The purpose of this quality improvement study was to
enhance the current knowledge level of oncology NPs
within an academic NCI-Designated Comprehensive Can-
cer Center related to quality measures in symptom man-

agement and end-of-life care; and support documenta-
tion of measures in an EHR by incorporating “Smart-
Phrases” (a documentation shortcut specific for the uni-
versity’s EHR software program that allows the typing
in of a “cue” phrase that will populate a larger body of
documentation) that capture quality measures content.
Improved implementation and documentation of care
processes pave the way for future measurement of patient
outcomes.
This project utilized quality measures that are included
in QOPI chart reviews. Neuss et al. (2005) reported that
by using the QOPI process, a rapid and objective measure-
ment of practice quality is obtained. QOPI has been shown
to provide a tool to practice self-examination that can pro-
mote excellence in cancer care.
Data sources
Approval for this research was obtained from the univer-
sity’s Institutional Review Board. Permission was also ob-
tained from ASCO to utilize QOPI measures to gather data.
The QOPI abstraction tool was modified to include specific
measures from modules that addressed pain, emotional
well-being, end-of-life care, and emetogenic chemother-
apy.
The database for the cancer center was queried for the
records of patients seen by Cancer Center Medical On-
cology NPs during the period of January to March 2012.
Data were not used from surgical oncology, radiation on-
cology, or bone marrow transplant. A random numbers
chart was used to assign NP charts between the two nurse
researchers who performed the independent chart au-
dits. Then, a random number chart was used to choose
five charts of patients seen by each NP for the preinter-

vention audit. Several test charts were reviewed by both
researchers to evaluate for interrater reliability that was
100%.
One hundred medical charts of Cancer Center patients
were retrospectively reviewed for documentation of se-
lect supportive care QOPI measures. This was completed
in August of 2012. Data were entered and analyzed using
SPSS software. Areas of deficiency in documentation were
identified and used to develop an educational intervention
(EI) for the NP staff. The areas to be used in the EI were
based on a selected 80% compliance level.
Based on this assessment, an intensive EI was devel-
oped. This incorporated a didactic presentation and in-
teractive case studies and was subsequently presented to
NPs within the Cancer Center. “SmartPhrases” were de-
veloped to support the documentation of the QOPI mea-
sures found to be below the established compliance level.
Reminder cards were developed listing the “SmartPhrases”
and given to all NPs during the educational sessions
(Figure 1). These “SmartPhrases” were developed by the
investigators by using the institution’s EHR personaliza-
tion tools (Epic, Verona, WI). To encourage attendance to
the presentations, multiple offerings were scheduled for
the presentations, food was offered at all sessions, and
a gift card was raffled off for attendees. A survey was
distributed to the NPs at the end of each session to eval-
uate their level of knowledge regarding QOPI measures
309
Improving documentation of quality measures P. Esper & S.
Walker

Figure 1 “SmartPhrase” reminder cards.
and their likelihood to utilize the “SmartPhrases” provided
to document these measures. Reminder e-mails were sent
out weekly to the NPs who attended the EI to promote
incorporation of measures into documentation.
Four weeks following the EI, another chart audit was
performed to assess the intervention’s effectiveness. Only
charts of those NPs that attended the educational session
were audited. Five charts per NP were audited for a total
of 65 charts being assessed using the same strict criteria as
prior. Analysis of post-EI data commenced in November,
2012.
Implications for practice
Descriptive statistics were used to evaluate data obtained
during the pre- and post-EI chart audits. SPSS and Excel
software programs were utilized for data management.
Pre-EI chart audit
The “pre” EI chart audit of 100 records was reviewed
for measures that fell below an 80% compliance level. The
authors adhered to a stringent and somewhat expanded
definition of each measure in an effort to identify where
intervention would be most meaningful. Those measures
falling below the established threshold included
� Documentation of the plan for addressing moderate
to severe pain.
� Appropriateness of the management plan for mod-
erate to severe pain.

� Assessment of narcotic efficacy on the return visit
following initial or prescription change.
� Assessment of bowel function at the time of narcotic
prescription.
� Assessment of bowel function postnarcotic prescrip-
tion.
� Assessment of emotional well-being.
� Plan for addressing emotional well-being, if indi-
cated.
Measures addressing oral chemotherapy management
also fell below the 80% level. A new oral chemotherapy
program was initiated following the preintervention chart
audit at the study institution. As a result, these measures
were not selected for inclusion in the EI as the investi-
gators felt this would introduce too many confounding
variables. Indicators regarding the appropriate supportive
measures for patients receiving moderate or highly emeto-
genic chemotherapy exceeded the threshold for inclusion
in the EI. Results of the pre-EI chart audits are detailed in
Figure 2.
EI
Overall, a total of 18 advanced practice nurses attended
one of the EIs (13 medical oncology NPs, two surgical on-
cology NPs, one psych oncology NP, one clinical nurse spe-
cialist, and one NP supervisor). A brief survey was admin-
istered to attendees following the session to evaluate their
initial impression of the information provided. Questions
and responses were as follows:

� Following this educational session, I have a bet-
ter understanding of what QOPI is: Yes—94%,
Somewhat—6%
� Following this educational session, I have an under-
standing of how using “SmartPhrases” can improve
my documentation: Yes—100%
� Following this educational session, I will use “Smart-
Phrases” to improve my documentation:
Yes—72%, Maybe—28%
� I believe that better documentation will improve pa-
tient care: Yes—78%, Somewhat—22%
The investigators found the NP staff who attended the
sessions to be interested in learning more about how qual-
ity measures could be incorporated into their documen-
tation. In general, they had minimal knowledge regarding
QOPI measures and did not realize that patient charts were
routinely being abstracted to evaluate documentation of
these measures. Several NP staff attending sessions asked
for results of their personal chart audit to be shared with
them.
Post-EI chart audit
Results of the post-EI chart audit are seen and
compared with preintervention chart audit findings in
Figure 2. Each of the nine indicators addressed dur-
ing the EI showed an improved level of compliance at
the time of the postintervention chart audits. While not
quantitatively measured, investigators observed that a
number of NPs appeared to utilize the “SmartPhrases”
from the reminder cards provided to them as part of the

310
P. Esper & S. Walker Improving documentation of quality
measures
Figure 2 Pre- and post-EI chart audit results.
Note. Results expressed in percentage of charts with completed
documentation (pre-EI, n = 100; post-EI, n = 65).
educational sessions. Once the “SmartPhrase” was placed
in a progress note, it can be modified. As a result, the
authors’ ability to quantify that the exact use of “Smart-
Phrases” was not possible.
The greatest degree of improvement was noted in doc-
umentation of measures to intervene for identified emo-
tional concerns (145% increase). In addition, a greater
than 70% improvement was seen in the documenta-
tion of an appropriate plan for pain management, the
effectiveness of the pain management intervention at
the subsequent visit, and evaluation of patient emotional
status.
The final results of this project were shared at a forum
for advance practice nurses throughout the authors’ work-
place. While this included “non-oncology” APNs, the in-
tent was to demonstrate how documentation of quality
measures can be impacted when an EHR is in use and
strategies that can be used to facilitate documentation. The
project was well received and future discussion may take
place with the EHR vendor to try and incorporate some
of these SmartPhrases into the current system more effi-
ciently.

Limitations
While the investigators in this study were very encour-
aged by the post-EI chart audit, several limitations to this
study are acknowledged. The sample utilized for the study
was small. It included only one academic institution and
focused on one provider segment—medical oncology NPs.
This alone makes the findings from the study difficult to
generalize to other institutions. It also increased the dif-
ficulty of finding appropriate charts for the chart audits.
In a large teaching facility, the NP may not always be the
provider to see the patient at subsequent visits. In addi-
tion, the investigators chose the QOPI measures believed
to be impacted most by NPs in the initial chart audit. This
may have also introduced bias and difficulty in the gen-
eralization of findings. Most importantly, the investigators
are NPs in the institution and well known to the partici-
pants in this study. Attendance at the educational session
was likely influenced by this. It is also plausible that the
improvement seen in the postchart audit was based on
the fact that the NPs in attendance were aware of the fact
that charts would be reaudited following the educational
session. This may have influenced their documentation of
measures addressed during the educational sessions via a
Hawthorn effect.
Another important limitation of this study was the intro-
duction of the new electronic medical record shortly prior
to the EI. Staff was admittedly stressed as a result of the
change in documentation. Asking them to participate in
this quality improvement process at the time it was offered
may have caused additional stress and affected outcomes.
The post-EI chart audit was completed in a time frame
relatively close to the intervention and an audit done 4–

6 months following the intervention may have resulted
in different findings, and also demonstrated whether
persistence in quality measure documentation had
occurred.
311
Improving documentation of quality measures P. Esper & S.
Walker
Conclusions
This project included a number of important goals. These
included determining the degree to which quality mea-
sures for symptom management and end-of-life care are
incorporated into NP practice, increasing NP knowledge
related to established oncology quality measures for symp-
tom management and end-of-life care, and evaluating
change in the use of established quality measures for
symptom management and end-of-life care following a de-
signed EI. While many administrative staff may believe
that nationally vetted standards are automatically part of
the staff’s knowledge base, this is often not the case. Qual-
ity measures, such as the QOPI measures, are not neces-
sarily discussed on a regular basis and institutional reports
may not be shared at the staff level. This study introduced
QOPI measures to the NP staff and increased their aware-
ness of how the measures were developed, the role of the
provider in documenting these measures, with an empha-
sis on the importance of the adage, “if it wasn’t docu-
mented, it wasn’t done.” Case studies using real patient
encounters allowed staff to see how the quality measures
could be readily incorporated into documentation and al-
low for improved continuity of care in future visits. Shar-

ing the deficits seen during the prechart audit with the NP
staff provided an impetus for them to think about their
own style and depth of documentation.
The time providers have for documentation is constantly
being impacted by the many other areas to be addressed
during a very time-limited patient encounter. Providing
staff with tools that can help expedite documentation has
the potential to improve the quality of information in the
medical record. Administrators would be well served to
evaluate the standards and quality measures that are per-
tinent to their organization as new EHRs are being imple-
mented and to strive to incorporate these into user tem-
plates. The attempt of this study to aid documentation of
quality measures by the use of “SmartPhrases” did prove
to be something that staff was able to incorporate into
their practice. By making these phrases easily accessible
and available to all staff, it increases the likelihood that im-
provement in the documentation of the quality measures
will be seen.
Providing quality oncology care to patients is a ma-
jor goal of care. A number of national guidelines, such
as the QOPI measures, have been created in an attempt to
establish standards for consistently providing quality pa-
tient care. Efforts should continue to evaluate the most
optimal ways for these measures to be implemented and
documented. Quality improvement projects, such as the
one described in this paper, represent an important step in
the process of improving patient outcomes.
Acknowledgments
The authors thank Constance Creech, RN, EdD,
ANP-BC, Associate Professor of Nursing, University

of Michigan-Flint, and Mary B. Killeen, RN, PhD,
CNAA-BC, Adjunct Associate Professor of Nursing, Uni-
versity of Michigan-Flint. This study was completed as
partial fulfillment of the DNP degree for the authors at
the University of Michigan-Flint. Drs. Creech and Killeen
served on the Capstone Committee for the authors.
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312
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