Whitepaper: Hospital Operations Management reduces wait states and replaces disparate hospital operations with a single system that functions like an air traffic control system.
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The document discusses the pervasive issue of waiting in healthcare, highlighting operational inefficiencies that contribute to prolonged wait states for patients and staff alike. It advocates for the implementation of Hospital Operations Management (HOM) systems to streamline processes, improve data integration, and facilitate evidence-based operational decisions, aiming to transform hospital environments into integrated, frictionless systems. The document emphasizes the urgency of addressing these challenges in light of rising healthcare costs and the potential for technological advancements to enable significant improvements in patient care delivery.
![Patients wait to be admitted. Doctors wait for test results.
Patients wait for treatment. Rooms wait to be cleaned. Nurses
wait for doctors. Doctors wait for equipment. Patients wait for
transport. Families wait for news. Patients wait to be discharged.
Everyone waits for someone or something.
Waiting is pervasive in healthcare today. A bird’s-eye view of care
delivery in most hospitals might resemble the start-stop quality of
our nation’s busiest expressways: discrete instances of productive
movement (a patient is triaged, a bed is filled, labs arrive, a nurse
gives medication instructions, surgery begins) separated by lengthy
“wait states” in which value-added activities come to a halt as the
operational systems grind, trying to keep pace with demand.
Waiting is symptomatic of not only the complexity of care delivery
but also the complex processes and disparate systems used to
coordinate that care. Too often, wait states have become the
status quo, to the frustration of patients, physicians, nurses, and
administrators.
This is not a new problem. In 1996, the Institute for Healthcare
Improvement identified wait times as one of healthcare’s biggest
challenges. “If we want to reduce delays and increase access, we
need to redesign the system that produces them. Better results,
lower costs, shorter waiting times, increased access—these will only
be achieved by changing the way we do our work.”1
Years later, there has been only incremental progress in achieving
that goal. Concerned about the continuing problem of ED
overcrowding and patient boarding, the Joint Commission issued
revised patient flow standards for 2013 and 2014, noting that,
“Although the ED may be where a patient flow problem manifests
in a hospital, the ED may not be the source of the problem” and
calling on hospital leaders to “better manage patient flow as a
hospitalwide concern.”2 Emergency room wait times have become
so emblematic of this issue, in fact, that they warrant their own tab
on HospitalCompare.hhs.gov.3
The “waiting problem” is not unique to the United States. Nor is
it unique to acute care settings. In a 2012 survey of outpatients
receiving cancer treatment4 at two hospitals in Sydney, Australia,
more than half of the respondents reported being concerned
about long wait times during one or more phases of their treatment.
A Press Ganey survey of 2.4 million U.S. patients in ambulatory care
settings showed that improved wait times correlated with better
patient satisfaction and financial performance.5
THE SAME ISSUES THAT PRODUCE WAITING
CAN LEAD TO PATIENT HARM
The OR is behind schedule. Cases are piling up. The anesthesiologist
is running late. Everyone is rushing. Things get missed.
The roots of patient harm are complex. In some cases, operational
inefficiency leads to distractions, interruptions, or inattention.
That operational “noise” can contribute to the risks of patient
harm—whether caused by poorly designed processes, inadequate
information flow, a mass casualty event, or inaccurate information
from untimely status updates—and prevent staff from doing the
right thing, despite their best intentions.
Many will remember a 2001 MRI accident in which a 6-year-old boy
was struck and killed by an oxygen canister. The proximate cause
was the ferrous projectile, but the root cause was operational chaos,
in that well-meaning professionals made a chain of disastrous
decisions due to gaps in policy, process, training, staff access,
communication, and other operational factors.6
Or consider hospital acquired infections (HAIs). It’s estimated that
20% to 40% of HAIs are transmitted to patients from hospital
employees.7 Despite years of attention and resources focused on
hand hygiene, caregivers still wash their hands only about half
the time when entering/leaving patient rooms.8 Heavy workloads,
staff shortages, inconveniently located wash stations, and limited
capability to monitor hand hygiene compliance—all operational
issues—are among the common root causes of poor compliance
rates. In addition, HAIs are a major driver of patient waiting in the
form of extended length of stay (LOS)—22 days on average versus
five days for a non-HAI patient.9
“Our patients are getting lost
in our complexity.”
“We operate like a set of silos instead
of an integrated hospital.”
N
olan, T.W., Schall, M.W., Berwick, D.M., Roessner, J. (1996). Reducing Delays and Waiting
Times Throughout the Healthcare System: Breathrough Series Guide. Boston: Institute
for Healthcare Improvement.
2
“The Joint Commission is asking why
our wait times are so high.”
1
R
3 Report: Requirement, Rationale, Reference. (2012). The Joint Commission.
Issue 4, December 19, 2012.
3
http://www.medicare.gov/HospitalCompare/compare.aspx#cmprTab=0cmprID=5200
62%2C520206stsltd=WIloc=53059lat=43.300827lng=-88.5371958AspxAutoDet
ectCookieSupport=1
4
P
aul, C., Carey, M., Anderson, A., Mackenzie, L., Sanson-Fisher, R., Courtney, R.,
Clinton-McHarg, T. (2012). Cancer patients’ concerns regarding access to cancer care:
perceived impact of waiting times along the diagnosis and treatment journey.
European Journal of Cancer Care 21:3, 21-329.
5
M
ichael, M., Schaffer, S.D., Egan, P.L., Little, B.B., Pritchard, P.S. (2013). Improving wait times
and patient satisfaction in primary care. Journal for Healthcare Quality 35:2, 50-60.
6
R
e-opening the Michael Colombini MRI Case via RCA. (2011). Reliability Center Inc.
http://www.reliability.com/demos/colobini_pres_2/colobini_pres_2.html
7
W
eber, D. J., Rutala, W. A., Miller, M. B., Huslage, K., Sickbert-Bennett, E. (2010).
Role of hospital surfaces in the transmission of emerging health care-associated
pathogens: norovirus, Clostridium difficile, and Acinetobacter species. Am J Infect
Control 38(5 Suppl 1),:S25-33. doi, 10.1016/j.ajic.2010.04.196.
8
M
cGuckin, M., Waterman, R., Govednik, J. (2009). Hand Hygiene Compliance Rates in
the United States – A One-Year Multicenter Collaboration Using Product/Volume Usage
Measurement and Feedback. Am J Medical Quality 24:3, 205-213.
9
M
urphy, D. and Whiting, J. (2007). Dispelling the Myths: the True Cost of
Healthcare-Associated Infections. Association for Professionals in Infection Control
and Epidemiology [Online.] http://www.premierinc.com/safety/topics/guide-lines/
downloads/09-hai-whitepaper.pdf](https://image.slidesharecdn.com/nowaitstateswhitepaper-140107173000-phpapp02/85/Whitepaper-Hospital-Operations-Management-reduces-wait-states-and-replaces-disparate-hospital-operations-with-a-single-system-that-functions-like-an-air-traffic-control-system-2-320.jpg)
![Disparate systems, fragmented care
Few organizations generate as much data as a hospital. But most
of that data—for obvious reasons—is clinical data necessary for
individual patient care. Operational data is not only far more limited
but also tends to be spread across multiple disparate systems, which
are difficult to integrate and not designed to work together.
As a result, managers do not have fully integrated tools to monitor
workflow, identify bottlenecks—before or as they occur—and take
action. Instead, managers are left to review data about historical
bottlenecks, delays, cancellations, and dissatisfied patients.
“Reimbursement rates are headed
down whether we like it or not.”
“There is no way that I can hit my new
operating expense target.”
“I need to get costs down now
without impacting patient safety.”
The time for operational transformation is now
GE believes that the emerging category of Hospital Operations
Management (HOM) systems may hold the answer. HOM replaces
disparate operational systems with a single control system that
functions in a similar manner to an air traffic control system—
acquiring, organizing, and presenting data from multiple sources
in a way that focuses the user on relevant performance characteristics
and supplies the necessary information to keep operations running
smoothly—across the enterprise.
HOM is a foundational technology on the order of and complementary
to the hospital information system (HIS) and electronic health record
(EHR). The goal of HOM is to enable evidence-based operations—the
complement to evidence-based medicine.
FIVE ATTRIBUTES OF HOM
In our view, HOM systems should be designed around five
characteristics: integration, transparency, actionability,
predictability, and simulation. These improve operational
hindsight, insight, and foresight.
Integration
HOM integrates data in real time from disparate hospital systems
to provide a single, continuously updated view across departments
and facilities and, for multi-hospital systems, campuses. This makes
it possible to monitor the interdependencies between departments,
caregivers, and support services.
Case in point: A large hospital near Chicago with a very busy Level 1
trauma center had the goal of reducing average length of stay (LOS)
and left-without-being-seen (LWBS) rates in the ED. Examination of
data revealed the problem arose from heavy discharge batching
on inpatient units in the late afternoon. By implementing a
housewide discharge optimization program, including need-focused
rounding and increasing pre-noon discharges by two to three
patients a day, the hospital was able to decrease ED diversion from
26 hours a month to near zero, and reduce LWBS rate to less than
1 percent. And even though surgical services were not a priority, the
new discharge process affected the post-anesthesia care unit (PACU),
where holds fell from 200 hours per month to nearly zero—further
testament to the value of a taking an integrated approach to
decision-making.
Transparency
Transparency in operational data means that a single view of the
truth is available to all stakeholders, from the caregiver to the
scheduler to the physician to the EVS manager to the patient’s
family in the waiting room.
Case in point: A medical center in the Southeast utilized an
automated RTLS system in a pilot study to support its hand hygiene
compliance program. Collecting data on more than 5,000 handwash
opportunities per day, the system yielded high-value metrics that
were used by leaders to analyze operational workflows and design
programs to improve compliance. In the eight weeks that the system
was in place, compliance percentage rose by nearly 30 points. “Our
belief is that this tracking technology will reinforce good behaviors
and get us to our target of zero infections [from poor hand hygiene
compliance],” said the hospital’s CEO.](https://image.slidesharecdn.com/nowaitstateswhitepaper-140107173000-phpapp02/85/Whitepaper-Hospital-Operations-Management-reduces-wait-states-and-replaces-disparate-hospital-operations-with-a-single-system-that-functions-like-an-air-traffic-control-system-3-320.jpg)
![Actionability
Actionability means that a recommendation is provided precisely
at the moment when it is needed. By applying nuanced and
advanced analytics in near real time, the HOM system moves beyond
data to decision support—proactively recommending a specific
action to affect patient flow right at that moment. Often this involves
pushing less but more useful data to front line staff and operational
leaders, while giving them the functionality to easily probe deeper
when merited. The system also provides alerts to prompt action
and escalate issues when conditions fall outside of desired
operational parameters.
Case in point: A large acute-care facility in the Southeast was
struggling with patient flow bottlenecks and high ED holding hours.
By combining real-time patient tracking data and smarter discharge
processes, the hospital was able reduce discharge time by 3,156
hours and reduce ED hold hours by 68% over nine months.
The difference, according to the Chief Nursing Officer, was the
immediacy of data through the HOM system: “We [used to] get
considerable data on patient flow … 24 hours after the fact.
[This] is real-time and it enables you to affect change and
movement right at that moment.”
Predictability
The HOM system should be designed so that the wealth of operational
data flowing into the system can be mined and analyzed to see
operational patterns. This intelligence can help leaders identify
demographic and utilization trends; anticipate staffing, equipment,
and infrastructure needs; and uncover potential operational issues
before they occur. An instructive example of this emerging class
of decision support analytics is an application that helps optimize
in-patient bed assignments so that resources are used efficiently
and effectively. Driven by a sophisticated yet customizable many-tomany optimization algorithm, the application helps bed managers
assign the right bed to the right patient at the right time—based on
the priorities and preferences of the individual institution.
Case in point: In a pilot project with a major health provider, the
application enabled more appropriate matching of bed attributes
to patient requirements and helped reduce bed assignment time
in the ED by three hours per patient. System data also was leveraged
to anticipate supply and demand for beds over the next 24 hours.
This information allowed managers to plan resource utilization, such
as EVS deployment, more effectively. With an accurate view of
prioritized constraints, both transactional optimization (which bed
should be cleaned first) and longer-term optimization (how should
staffing levels be adjusted for the next shift) can be realized.
Simulation
A full-featured HOM solution should include simulation software
to allow leaders to model the hospital’s workflows and to test
nuanced alternative scenarios. Modern discrete event-based
simulation tools enable leaders to test the impact of a range of
operational decisions—expanded services, staffing models, care
delivery processes—in order to understand their likely impact and
make better decisions. With an HOM, the simulation engine is fed
by real-time location awareness data. That keeps it current and
makes it much more accurate than with observation data alone.
For example, consider a hospital that wants to expand its orthopedic
service by bringing on more spine specialists. The model could
simulate the effects of increased flow of spine patients on the entire
organization, providing information to help predict bottlenecks
and gaps, identify units that could be repurposed to handle
incremental volume, flag staff that would need to be cross-trained
or upskilled, and so on.
Case in point: A large teaching hospital wanted to better utilize
their fixed infrastructure relative to patient flow in order to support
projected growth. Multiple capacity scenarios were developed,
factoring in growth objectives, utilization data, demand projections,
and efficiency targets. These scenarios were tested and validated
with proprietary simulation modeling to determine how various
changes—such as opening and closing capacity for operating rooms
or redistributing daily discharge times or changing the bed mix—
might affect staffing, workflow, and the operational cost structure.
The “winning” operational changes that emerged enabled the
hospital to free up OR capacity for 750 additional cases per year
(while shuttering two rooms) and reduce average LOS by 0.35 days.
The Chief Nursing Officer pointed to the data integration value of
HOM technology as being the lever of change: “We can now draw
data from multiple information sources and make nonlinear
references between seemingly disparate areas of the organization
to better manage capacity today and in the future.”](https://image.slidesharecdn.com/nowaitstateswhitepaper-140107173000-phpapp02/85/Whitepaper-Hospital-Operations-Management-reduces-wait-states-and-replaces-disparate-hospital-operations-with-a-single-system-that-functions-like-an-air-traffic-control-system-4-320.jpg)
Whitepaper: Hospital Operations Management reduces wait states and replaces disparate hospital operations with a single system that functions like an air traffic control system.
- 1. GE Healthcare No Wait States … in pursuit of the frictionless patient experience. Electronic health records have fallen short. Patients continue to wait. Costs remain high. Why focusing on operational management can help hospitals make things right … starting now. By: Francis X. Dirksmeier Jeff Terry
- 2. Patients wait to be admitted. Doctors wait for test results. Patients wait for treatment. Rooms wait to be cleaned. Nurses wait for doctors. Doctors wait for equipment. Patients wait for transport. Families wait for news. Patients wait to be discharged. Everyone waits for someone or something. Waiting is pervasive in healthcare today. A bird’s-eye view of care delivery in most hospitals might resemble the start-stop quality of our nation’s busiest expressways: discrete instances of productive movement (a patient is triaged, a bed is filled, labs arrive, a nurse gives medication instructions, surgery begins) separated by lengthy “wait states” in which value-added activities come to a halt as the operational systems grind, trying to keep pace with demand. Waiting is symptomatic of not only the complexity of care delivery but also the complex processes and disparate systems used to coordinate that care. Too often, wait states have become the status quo, to the frustration of patients, physicians, nurses, and administrators. This is not a new problem. In 1996, the Institute for Healthcare Improvement identified wait times as one of healthcare’s biggest challenges. “If we want to reduce delays and increase access, we need to redesign the system that produces them. Better results, lower costs, shorter waiting times, increased access—these will only be achieved by changing the way we do our work.”1 Years later, there has been only incremental progress in achieving that goal. Concerned about the continuing problem of ED overcrowding and patient boarding, the Joint Commission issued revised patient flow standards for 2013 and 2014, noting that, “Although the ED may be where a patient flow problem manifests in a hospital, the ED may not be the source of the problem” and calling on hospital leaders to “better manage patient flow as a hospitalwide concern.”2 Emergency room wait times have become so emblematic of this issue, in fact, that they warrant their own tab on HospitalCompare.hhs.gov.3 The “waiting problem” is not unique to the United States. Nor is it unique to acute care settings. In a 2012 survey of outpatients receiving cancer treatment4 at two hospitals in Sydney, Australia, more than half of the respondents reported being concerned about long wait times during one or more phases of their treatment. A Press Ganey survey of 2.4 million U.S. patients in ambulatory care settings showed that improved wait times correlated with better patient satisfaction and financial performance.5 THE SAME ISSUES THAT PRODUCE WAITING CAN LEAD TO PATIENT HARM The OR is behind schedule. Cases are piling up. The anesthesiologist is running late. Everyone is rushing. Things get missed. The roots of patient harm are complex. In some cases, operational inefficiency leads to distractions, interruptions, or inattention. That operational “noise” can contribute to the risks of patient harm—whether caused by poorly designed processes, inadequate information flow, a mass casualty event, or inaccurate information from untimely status updates—and prevent staff from doing the right thing, despite their best intentions. Many will remember a 2001 MRI accident in which a 6-year-old boy was struck and killed by an oxygen canister. The proximate cause was the ferrous projectile, but the root cause was operational chaos, in that well-meaning professionals made a chain of disastrous decisions due to gaps in policy, process, training, staff access, communication, and other operational factors.6 Or consider hospital acquired infections (HAIs). It’s estimated that 20% to 40% of HAIs are transmitted to patients from hospital employees.7 Despite years of attention and resources focused on hand hygiene, caregivers still wash their hands only about half the time when entering/leaving patient rooms.8 Heavy workloads, staff shortages, inconveniently located wash stations, and limited capability to monitor hand hygiene compliance—all operational issues—are among the common root causes of poor compliance rates. In addition, HAIs are a major driver of patient waiting in the form of extended length of stay (LOS)—22 days on average versus five days for a non-HAI patient.9 “Our patients are getting lost in our complexity.” “We operate like a set of silos instead of an integrated hospital.” N olan, T.W., Schall, M.W., Berwick, D.M., Roessner, J. (1996). Reducing Delays and Waiting Times Throughout the Healthcare System: Breathrough Series Guide. Boston: Institute for Healthcare Improvement. 2 “The Joint Commission is asking why our wait times are so high.” 1 R 3 Report: Requirement, Rationale, Reference. (2012). The Joint Commission. Issue 4, December 19, 2012. 3 http://www.medicare.gov/HospitalCompare/compare.aspx#cmprTab=0cmprID=5200 62%2C520206stsltd=WIloc=53059lat=43.300827lng=-88.5371958AspxAutoDet ectCookieSupport=1 4 P aul, C., Carey, M., Anderson, A., Mackenzie, L., Sanson-Fisher, R., Courtney, R., Clinton-McHarg, T. (2012). Cancer patients’ concerns regarding access to cancer care: perceived impact of waiting times along the diagnosis and treatment journey. European Journal of Cancer Care 21:3, 21-329. 5 M ichael, M., Schaffer, S.D., Egan, P.L., Little, B.B., Pritchard, P.S. (2013). Improving wait times and patient satisfaction in primary care. Journal for Healthcare Quality 35:2, 50-60. 6 R e-opening the Michael Colombini MRI Case via RCA. (2011). Reliability Center Inc. http://www.reliability.com/demos/colobini_pres_2/colobini_pres_2.html 7 W eber, D. J., Rutala, W. A., Miller, M. B., Huslage, K., Sickbert-Bennett, E. (2010). Role of hospital surfaces in the transmission of emerging health care-associated pathogens: norovirus, Clostridium difficile, and Acinetobacter species. Am J Infect Control 38(5 Suppl 1),:S25-33. doi, 10.1016/j.ajic.2010.04.196. 8 M cGuckin, M., Waterman, R., Govednik, J. (2009). Hand Hygiene Compliance Rates in the United States – A One-Year Multicenter Collaboration Using Product/Volume Usage Measurement and Feedback. Am J Medical Quality 24:3, 205-213. 9 M urphy, D. and Whiting, J. (2007). Dispelling the Myths: the True Cost of Healthcare-Associated Infections. Association for Professionals in Infection Control and Epidemiology [Online.] http://www.premierinc.com/safety/topics/guide-lines/ downloads/09-hai-whitepaper.pdf
- 3. Disparate systems, fragmented care Few organizations generate as much data as a hospital. But most of that data—for obvious reasons—is clinical data necessary for individual patient care. Operational data is not only far more limited but also tends to be spread across multiple disparate systems, which are difficult to integrate and not designed to work together. As a result, managers do not have fully integrated tools to monitor workflow, identify bottlenecks—before or as they occur—and take action. Instead, managers are left to review data about historical bottlenecks, delays, cancellations, and dissatisfied patients. “Reimbursement rates are headed down whether we like it or not.” “There is no way that I can hit my new operating expense target.” “I need to get costs down now without impacting patient safety.” The time for operational transformation is now GE believes that the emerging category of Hospital Operations Management (HOM) systems may hold the answer. HOM replaces disparate operational systems with a single control system that functions in a similar manner to an air traffic control system— acquiring, organizing, and presenting data from multiple sources in a way that focuses the user on relevant performance characteristics and supplies the necessary information to keep operations running smoothly—across the enterprise. HOM is a foundational technology on the order of and complementary to the hospital information system (HIS) and electronic health record (EHR). The goal of HOM is to enable evidence-based operations—the complement to evidence-based medicine. FIVE ATTRIBUTES OF HOM In our view, HOM systems should be designed around five characteristics: integration, transparency, actionability, predictability, and simulation. These improve operational hindsight, insight, and foresight. Integration HOM integrates data in real time from disparate hospital systems to provide a single, continuously updated view across departments and facilities and, for multi-hospital systems, campuses. This makes it possible to monitor the interdependencies between departments, caregivers, and support services. Case in point: A large hospital near Chicago with a very busy Level 1 trauma center had the goal of reducing average length of stay (LOS) and left-without-being-seen (LWBS) rates in the ED. Examination of data revealed the problem arose from heavy discharge batching on inpatient units in the late afternoon. By implementing a housewide discharge optimization program, including need-focused rounding and increasing pre-noon discharges by two to three patients a day, the hospital was able to decrease ED diversion from 26 hours a month to near zero, and reduce LWBS rate to less than 1 percent. And even though surgical services were not a priority, the new discharge process affected the post-anesthesia care unit (PACU), where holds fell from 200 hours per month to nearly zero—further testament to the value of a taking an integrated approach to decision-making. Transparency Transparency in operational data means that a single view of the truth is available to all stakeholders, from the caregiver to the scheduler to the physician to the EVS manager to the patient’s family in the waiting room. Case in point: A medical center in the Southeast utilized an automated RTLS system in a pilot study to support its hand hygiene compliance program. Collecting data on more than 5,000 handwash opportunities per day, the system yielded high-value metrics that were used by leaders to analyze operational workflows and design programs to improve compliance. In the eight weeks that the system was in place, compliance percentage rose by nearly 30 points. “Our belief is that this tracking technology will reinforce good behaviors and get us to our target of zero infections [from poor hand hygiene compliance],” said the hospital’s CEO.
- 4. Actionability Actionability means that a recommendation is provided precisely at the moment when it is needed. By applying nuanced and advanced analytics in near real time, the HOM system moves beyond data to decision support—proactively recommending a specific action to affect patient flow right at that moment. Often this involves pushing less but more useful data to front line staff and operational leaders, while giving them the functionality to easily probe deeper when merited. The system also provides alerts to prompt action and escalate issues when conditions fall outside of desired operational parameters. Case in point: A large acute-care facility in the Southeast was struggling with patient flow bottlenecks and high ED holding hours. By combining real-time patient tracking data and smarter discharge processes, the hospital was able reduce discharge time by 3,156 hours and reduce ED hold hours by 68% over nine months. The difference, according to the Chief Nursing Officer, was the immediacy of data through the HOM system: “We [used to] get considerable data on patient flow … 24 hours after the fact. [This] is real-time and it enables you to affect change and movement right at that moment.” Predictability The HOM system should be designed so that the wealth of operational data flowing into the system can be mined and analyzed to see operational patterns. This intelligence can help leaders identify demographic and utilization trends; anticipate staffing, equipment, and infrastructure needs; and uncover potential operational issues before they occur. An instructive example of this emerging class of decision support analytics is an application that helps optimize in-patient bed assignments so that resources are used efficiently and effectively. Driven by a sophisticated yet customizable many-tomany optimization algorithm, the application helps bed managers assign the right bed to the right patient at the right time—based on the priorities and preferences of the individual institution. Case in point: In a pilot project with a major health provider, the application enabled more appropriate matching of bed attributes to patient requirements and helped reduce bed assignment time in the ED by three hours per patient. System data also was leveraged to anticipate supply and demand for beds over the next 24 hours. This information allowed managers to plan resource utilization, such as EVS deployment, more effectively. With an accurate view of prioritized constraints, both transactional optimization (which bed should be cleaned first) and longer-term optimization (how should staffing levels be adjusted for the next shift) can be realized. Simulation A full-featured HOM solution should include simulation software to allow leaders to model the hospital’s workflows and to test nuanced alternative scenarios. Modern discrete event-based simulation tools enable leaders to test the impact of a range of operational decisions—expanded services, staffing models, care delivery processes—in order to understand their likely impact and make better decisions. With an HOM, the simulation engine is fed by real-time location awareness data. That keeps it current and makes it much more accurate than with observation data alone. For example, consider a hospital that wants to expand its orthopedic service by bringing on more spine specialists. The model could simulate the effects of increased flow of spine patients on the entire organization, providing information to help predict bottlenecks and gaps, identify units that could be repurposed to handle incremental volume, flag staff that would need to be cross-trained or upskilled, and so on. Case in point: A large teaching hospital wanted to better utilize their fixed infrastructure relative to patient flow in order to support projected growth. Multiple capacity scenarios were developed, factoring in growth objectives, utilization data, demand projections, and efficiency targets. These scenarios were tested and validated with proprietary simulation modeling to determine how various changes—such as opening and closing capacity for operating rooms or redistributing daily discharge times or changing the bed mix— might affect staffing, workflow, and the operational cost structure. The “winning” operational changes that emerged enabled the hospital to free up OR capacity for 750 additional cases per year (while shuttering two rooms) and reduce average LOS by 0.35 days. The Chief Nursing Officer pointed to the data integration value of HOM technology as being the lever of change: “We can now draw data from multiple information sources and make nonlinear references between seemingly disparate areas of the organization to better manage capacity today and in the future.”
- 5. Realizing the potential of No Wait State Clearly, much of what constitutes the Hospital Operations Management discussion is not new. The approach focuses on long-standing issues, and the concepts of what can be done—reducing discharge cycle times, driving greater accountability in handwashing, streamlining surgical scheduling, and so on—are not revolutionary. What is different, however, is new urgency and new means for action. The overwhelming cost pressures in healthcare today make it impossible not to address the wait-states problem. We need to act now. And technology is changing what is possible. Powered by an intelligent software platform that allows dynamic real-time data collection and discrete event-based, simulation-enabled prediction, HOM is the game-changer that healthcare leaders have needed to transform operations. Instead of making decisions with a narrow view of the current situation, HOM provides a complete picture to decision-makers working to bring the resources together to deliver great care. Imagine the impact if we succeed and the no-wait state becomes the norm for hospital operations. Imagine a time when the entire hospital functions as an integrated team … when variances and delays are the exception … when peaks and valleys of demand are encountered infrequently and managed proactively … when operational friction doesn’t contribute to the risks of patient harm … when patients don’t expect to wait.
- 6. About GE Healthcare GE Healthcare provides transformational medical technologies and services that are shaping a new age of patient care. Our broad expertise in medical imaging and information technologies, medical diagnostics, patient monitoring systems, drug discovery, biopharmaceutical manufacturing technologies, performance improvement and performance solutions services help our customers to deliver better care to more people around the world at a lower cost. In addition, we partner with healthcare leaders, striving to leverage the global policy change necessary to implement a successful shift to sustainable healthcare systems. Our “healthymagination” vision for the future invites the world to join us on our journey as we continuously develop innovations focused on reducing costs, increasing access and improving quality around the world. Headquartered in the United Kingdom, GE Healthcare is a unit of General Electric Company (NYSE: GE). Worldwide, GE Healthcare employees are committed to serving healthcare professionals and their patients in more than 100 countries. For more information about GE Healthcare, visit our website at www.gehealthcare.com. GE Healthcare 3000 North Grandview Waukesha, WI 53188 USA Francis X. Dirksmeier is general manager for Global Asset Management at GE Healthcare. He’s recognized in the healthcare industry as a visionary leader and a strategic entrepreneur with a track record for bringing out the best in people to drive growth and operational excellence. Previously, Fran co-founded Agility Healthcare Solutions and grew the company into a multi-million-dollar venture that solved critical equipment management and patient flow problems. Prior to that, Fran was president of McKesson Medical-Surgical, a division of McKesson Corp that was focused on acute care, primary care, and long-term care business segments. Contact: Francis.Dirksmeier@ge.com Jeff Terry is general manager for Solution Development at GE Healthcare Performance Solutions. Jeff and his team develop solutions that combine consulting and technology to help providers improve operational performance in the real-world delivery of care. Focus areas include patient safety, capacity management, hospital design, care pathway design, leadership development, and management systems. Jeff earned a B.E. in electrical engineering from Vanderbilt University, an MBA from Marquette University, and is a Fellow of the American College of Healthcare Executives. Prior to joining GE, Jeff served as a Captain in the U.S. Army Corps of Engineers. Contact: Jeffrey.Terry@ge.com ©2013 General Electric Company – All rights reserved. GE and GE Monogram are trademarks of General Electric Company. GE Healthcare, a division of General Electric Company