![terms by way of mapping. The last part was about the development and implementation of MyHarmony
to show that the application can codify relevant terms in free-text using Natural Language Processing
(NLP) technique. The SNOMED CT codified data can then be analysed for information generation.
2. Methodology:
The development was first started in 2014 with the development of Cardiology Refset. Cardiology Refset
was the terminology reference for the MyHarmony engine during the harmonisation/mapping and
codification process. Cardiology Refset (version 1.0) was completed and released in 2014. It is a simple
reference set [1] containing about 600 terms related to Cardiology speciality including signs and
symptoms, diagnoses, procedures, body structures, medical devices and medications. It was delivered in
time to be tested on MyHarmony standalone system to generate National Cardiovascular Disease
(NCVD) registries.
The draft Refset and method was presented during IHTSDO meetings and Expo in succession on
September 2013, October 2013, and April 2014 to gain feedback from experts in the international
community. The finalized method was presented during SNOMED CT Expo, October 2014 [2] .
The Cardiology Refset was then expanded to include all cardiology related terms and Cardiology Refset
v1.1 was completed in July 2016 containing more than 6000 concepts. First, more than 300,000
SNOMED CT concepts (Fully Specified Names) were extracted and reviewed by PIK using eyeballing
technique. About 12,000 concepts that were believed to be related to Cardiology specialty was given to
the clinicians for review. The clinicians reduced the number of concepts to about 6,000. Additionally, the
Refset included local terms and common abbreviations which were mapped to existing concepts.
Next, the team utilise MyHarmony to generate the analysis. There were 4 main functions in MyHarmony:
1. Terminology Management– to allow user to upload the SNOMED CT International Release
content into MyHarmony, and upload SNOMED CT Refset in reference to the SNOMED CT
International Release.
2. Data Management– to allow user to upload the data that will be harmonized and codified. This
function also allows user to view the content of the data.
3. Codification Management– to allow user to codify the dataset according to the selected
SNOMED CT Refset and view the codified data for validation purpose.
4. Query Management– to allow user to explore the data by generating queries using Structured
Query Language (SQL).
The functions were arranged according to the work process. First, the SNOMED CT International
Release, SNOMED CT Cardiology Refset, and the dataset needs to be uploaded. Then, the dataset is
codified and saved. Using the Query Management, the codified data can then be explored via data
profiling and query generation.
For initial analysis, SNOMED CT International release version 20160731 was used as the Cardiology
Reference Set was developed using this version of SNOMED CT.](https://image.slidesharecdn.com/myharmony-generatingstatisticsfromclinicaltextformonitoringclinicalqualityindicators-211217080339/85/My-harmony-generating-statistics-from-clinical-text-for-monitoring-clinical-quality-indicators-2-320.jpg)
![suitable combinations of words in a sentence. With further iterations and improvement in the mapping,
these challenges were overcome[3].
From the SNOMED CT codified database, the system was able to show a more accurate result during
analysis . This is because MyHarmony capitalises on the existing SNOMED CT relationships structure
between concepts. In this case, when querying “Number of Ischaemic Heart Disease cases per year”,
MyHarmony search the code and term for “Ischaemic heart disease”, its synonyms and accepted
abbreviations, and all the subtypes of Ischaemic heart disease such as all subtypes of “Myocardial
infarction” and “Angina”. MyHarmony aggregates these records resulting in a more accurate analysis.
Usually, the result where MyHarmony utilise SNOMED CT‟s relationship structure would show more
records. This is because Mi-Harmony was able to aggregate data not just through String match, but also
utilize the IS-A hierarchy structure in SNOMED CT. For example, querying “Ischemic heart disease” will
gather clinical records with synonymous terms like “Ischaemic heart disease” and “IHD”; as well as
clinical records containing all the subtypes of “Ischemic heart disease” such as “Myocardial Infarction”
and “Unstable angina”.
Context awareness such as negation and pasts events were also applied. For example, the term “No chest
pain”, “No known history of diabetes mellitus”, and “Symptoms of heart failure” will not be coded as the
presenting condition. Additionally, terms like “Previous history of”, “Previous admission of”, and
“Family history of” within the same sentence as a clinical condition will not be coded as the current
condition for the record.
4. Discussion and Conclusion:
When showcasing these abilities to the clinicians, the team agreed that MyHarmony was able to:
(i) Generate more information from free-text utilising the SNOMED CT structure, thus,
reducing the effort needed to collect data in a structured manner such as in a registry
and indicator reports;
(ii) Able to generate new information by retrospectively running new queries on old
discharge summary records; thus, reducing the effort and time to collect data in a
prospective manner when new questions arise, such as for indicator reports that often
change on a yearly basis;
(iii) Able to deliver information in a timelier fashion; thus clinicians and health managers
are able to plan and take action without waiting for a 1 to 3 yearly report;
(iv) Improve documentation of clinicians when they are aware of MyHarmony‟s ability
during roadshows.
Generating indicators for monitoring and evaluation can be a burden even for healthcare facilities
equipped with EHR. Conventionally, collecting data for indicators requires multiple data entries in
aggregated manner, with manual submission to central agencies, where the results are only published on a
yearly basis. Introducing MyHarmony may reduce these burdens. Capturing data from the source in an
automated way, i.e. free text documented by doctors, would reduce duplication of work and the amount of
resources to capture the data into manual form. Having the data in granular form would allow a more](https://image.slidesharecdn.com/myharmony-generatingstatisticsfromclinicaltextformonitoringclinicalqualityindicators-211217080339/85/My-harmony-generating-statistics-from-clinical-text-for-monitoring-clinical-quality-indicators-4-320.jpg)
![dynamic analysis and prevents dishonesty. Information required, whether old or new information, can be
formulated and disseminated back to the clinicians and health managers in a timelier fashion.
MyHarmony has the potential to expand further in its implementation and technology. However, there are
still challenges to be addressed. Currently, MyHarmony has been developed to mine free-text for
Cardiology via a back-end approach. It uses a single version of SNOMED CT International. The team is
still researching the best approach to manage SNOMED CT versions and its codified data, which may
impact the resulting analysis in an inconsistent way. The team are also seeking international experience
for this matter.
Other challenges include researching a more efficient and effective method to develop SNOMED CT
Refsets. The initial method by referencing terms required by registries or indicators has been established.
However, expanding the SNOMED CT Refsets to include relevant terms for a specific clinical specialty
or domain needs to be refined further. Eye-balling technique to search the entire SNOMED CT content
have its strength and weaknesses. Even though it is a very thorough method, there are possibilities of
missed terms and very time consuming. Despite these challenges, the journey in developing MyHarmony
and the lessons learnt has allowed the team to refine the methods and processes to expand the use of
MyHarmony to other clinical specialties.
Analysis from unstructured data would hope to complement analysis from structured data (like census and
registries), with the additional benefit workload reduction to provide timelier, trusted, and dynamic
information.
References:
1. SNOMED CT Simple Reference Set:
https://confluence.ihtsdotools.org/display/DOCRFSPG/5.1.+Simple+Reference+Set
2. Mohd Sulaiman I, Sheikh Ahmad MK. SNOMED CT Cardiology Reference Set Development,
Malaysia. Proceedings of the SNOMED CT Implementation Showcase [Internet]. Amsterdam: IHTSDO;
2014. Retrieved from:
http://ihtsdo.org/fileadmin/user_upload/doc/showcase/show14/SnomedCtShowcase2014_Abstract_14058
.pdf
3. Abdul Manaf NA, Mohamed K, Lukose D. Harmonizing EHR Databases with SNOMED CT. Proceedings of the
SNOMED CT Implementation Showcase 2014 [Internet]. Amsterdam: IHTSDO; 2014.
https://confluence.ihtsdotools.org/display/FT/SNOMED+CT+Implementation+Showcase+2014](https://image.slidesharecdn.com/myharmony-generatingstatisticsfromclinicaltextformonitoringclinicalqualityindicators-211217080339/85/My-harmony-generating-statistics-from-clinical-text-for-monitoring-clinical-quality-indicators-5-320.jpg)
My harmony generating statistics from clinical text for monitoring clinical quality indicators
- 1. MyHarmony: Generating Statistics from Clinical Text for Monitoring Clinical Quality Indicators Md. Khadzir Sheikh Ahmad1 ; Mohd Syazrin Mohd Sakri1 ; „Ismat Mohd Sulaiman1 ; Syirahaniza Mohd Salleh1 ; Dickson Lukose2 ; Omar Ismail1 ; Abd Aziz Latip3 ; Muhammad Aiman Mazlan3 . 1 Health Informatics Centre, Planning Division, Ministry of Health, Malaysia 2 GCS Agile Pty. Ltd. 3 MIMOS Bhd. Abstract: The Ministry of Health developed MyHarmony with MIMOS Bhd. as part of the Malaysian Health Data Warehouse (MyHDW) initiative. MyHDW aims to be a trusted source of truth of comprehensive health data structured for analysis. MyHarmony fulfills that criteria by enabling data and information from unstructured form to be mined, such as texts, images, and sound. MyHarmony's first deliverable is the ability to mine clinical texts using Natural Language Processing (NLP) with SNOMED CT as its knowledge-base of clinical terms. MyHarmony is the engine the retrieves data and information into computer processable form by assigning SNOMED CT codes, which can then be further analysed statistically. MyHarmony is able to recognise and harmonise different terms that means the same. It also understands context for a more accurate coding; such as negations (no, not known, unknown) and conditionals (past history, symptoms of, previous). Using SNOMED CT, MyHarmony's ability is further advanced by using subsumption technique for a more comprehensive statistical results. This study will present a use case where clinical text from anonymized hospital discharge summaries can generate clinical indicators using MyHarmony for health managers. An added benefit to the operational (hospital) staff is the ability to produce such indicators is an efficient and timely manner by reducing workload for data collection and submission. MyHarmony could be the new and improved way to provide important statistical measures for evidence-based health planning, leading to improved healthcare services and health as a whole. Keywords: MyHarmony; MyHDW; Text mining; Quality Indicators; SNOMED CT 1. Introduction: The Ministry of Health developed MyHarmony with MIMOS Bhd. as part of the Malaysian Health Data Warehouse (MyHDW) initiative. MyHDW aims to be a trusted source of truth of comprehensive health data structured for analysis. MyHarmony is an application in the Malaysian Health Data Warehouse (MyHDW) that aims to analyse semi-structured and unstructured data. The unstructured data can be in the form of free-text, visual, audio and machine generated data. Unstructured data does not have predetermined values and not stored in an organized manner to be analysed by a conventional data warehouse. Therefore, other techniques need to be applied. MyHarmony aims to address this and be included as part of MyHDW. There were three (3) major deliverables in the conceptual stage. The first part refers to the development and implementation of health terminology standards, namely SNOMED CT, which will be the knowledge bases for MyHarmony. The second part was harmonization of the medical terminology to SNOMED CT
- 2. terms by way of mapping. The last part was about the development and implementation of MyHarmony to show that the application can codify relevant terms in free-text using Natural Language Processing (NLP) technique. The SNOMED CT codified data can then be analysed for information generation. 2. Methodology: The development was first started in 2014 with the development of Cardiology Refset. Cardiology Refset was the terminology reference for the MyHarmony engine during the harmonisation/mapping and codification process. Cardiology Refset (version 1.0) was completed and released in 2014. It is a simple reference set [1] containing about 600 terms related to Cardiology speciality including signs and symptoms, diagnoses, procedures, body structures, medical devices and medications. It was delivered in time to be tested on MyHarmony standalone system to generate National Cardiovascular Disease (NCVD) registries. The draft Refset and method was presented during IHTSDO meetings and Expo in succession on September 2013, October 2013, and April 2014 to gain feedback from experts in the international community. The finalized method was presented during SNOMED CT Expo, October 2014 [2] . The Cardiology Refset was then expanded to include all cardiology related terms and Cardiology Refset v1.1 was completed in July 2016 containing more than 6000 concepts. First, more than 300,000 SNOMED CT concepts (Fully Specified Names) were extracted and reviewed by PIK using eyeballing technique. About 12,000 concepts that were believed to be related to Cardiology specialty was given to the clinicians for review. The clinicians reduced the number of concepts to about 6,000. Additionally, the Refset included local terms and common abbreviations which were mapped to existing concepts. Next, the team utilise MyHarmony to generate the analysis. There were 4 main functions in MyHarmony: 1. Terminology Management– to allow user to upload the SNOMED CT International Release content into MyHarmony, and upload SNOMED CT Refset in reference to the SNOMED CT International Release. 2. Data Management– to allow user to upload the data that will be harmonized and codified. This function also allows user to view the content of the data. 3. Codification Management– to allow user to codify the dataset according to the selected SNOMED CT Refset and view the codified data for validation purpose. 4. Query Management– to allow user to explore the data by generating queries using Structured Query Language (SQL). The functions were arranged according to the work process. First, the SNOMED CT International Release, SNOMED CT Cardiology Refset, and the dataset needs to be uploaded. Then, the dataset is codified and saved. Using the Query Management, the codified data can then be explored via data profiling and query generation. For initial analysis, SNOMED CT International release version 20160731 was used as the Cardiology Reference Set was developed using this version of SNOMED CT.
- 3. The team received a set of database from a hospital with cardiology service which consists of 16224 discharge summaries from year 2017. The database was then uploaded into MyHarmony. The personally identified information (patient names, ID, and street address) were anonymised prior to codification and analysis. The output is a codified dataset, which enable information processing and analysis by machines. Using the Query Management, the codified data was then be explored via data profiling and query generation. 3. Result: The team conducted several data profiling queries to ensure that MyHarmony were able to capture the data correctly. For example, the number of records by month between Raw data (MyHarmony without SNOMED CT) and Harmonized data (MyHarmony with SNOMED CT) should return the same result. Other examples of data profiling queries are the number of records by gender, by specialty, and by ethnicity. Next, the team developed queries required by the National Cardiovascular Disease (NCVD) registries and compare the results with published registry reports. For example, querying the number of Ischaemic Heart Disease (IHD) by gender shows 1:4 female to male ratio, which is a similar ratio in the registry reports. Furthermore, the query also shows that Harmonized data captures more result compare to Raw data due to SNOMED CT relationship structure, thus capturing all the subtypes of IHD and its synonyms or ways of writing. The registry, however, only captures three (3) diagnosis due to its structured format, which are ST Elevation Myocardial Infarction, Non-ST Elevation Myocardial Infarction, and Unstable Angina. This trend and pattern comparison allow validation by the Clinicians and gains their buy-in in using MyHarmony. The team also tried to generate more queries required by the NCVD registry. However, it was limited by the documentation in the discharge summary. Registry queries requires more detail information that may often not documented in a discharge summary, such as information on smoking status and complications of procedures. After that, the team was tasked to generate National Cardiology Key Performance Indicators (KPIs). MyHarmony are able to generate 7 out of the 8 KPIs (KPI 2 to 8). The first KPI was excluded because the data are available at the clinic and not documented in inpatient discharge summaries. The Health Information Framework (HIF) was developed for the 7 KPIs, which detailed out the inclusion and exclusion criteria, the target, the formula, the terms used by MyHarmony, and query, and lastly a section for additional notes. Preliminary manual validation on the completeness and accuracy of codified data shows 90% precision and 70% recall. The content of those records is complex as it does not follow grammar rules, and contains a large number of short forms, abbreviations, acronyms and analogous terms (e.g., synd, ACS, CCS IV, NYHA 2). One example of record is “2VD with RCA culprit lesion - Ad hoc PCI DES to RCA and LAD” which is challenging to codify using approaches based on strict grammar. The revised version of MyHarmony uses a different approach based on shallow parsing and the consideration of multiple
- 4. suitable combinations of words in a sentence. With further iterations and improvement in the mapping, these challenges were overcome[3]. From the SNOMED CT codified database, the system was able to show a more accurate result during analysis . This is because MyHarmony capitalises on the existing SNOMED CT relationships structure between concepts. In this case, when querying “Number of Ischaemic Heart Disease cases per year”, MyHarmony search the code and term for “Ischaemic heart disease”, its synonyms and accepted abbreviations, and all the subtypes of Ischaemic heart disease such as all subtypes of “Myocardial infarction” and “Angina”. MyHarmony aggregates these records resulting in a more accurate analysis. Usually, the result where MyHarmony utilise SNOMED CT‟s relationship structure would show more records. This is because Mi-Harmony was able to aggregate data not just through String match, but also utilize the IS-A hierarchy structure in SNOMED CT. For example, querying “Ischemic heart disease” will gather clinical records with synonymous terms like “Ischaemic heart disease” and “IHD”; as well as clinical records containing all the subtypes of “Ischemic heart disease” such as “Myocardial Infarction” and “Unstable angina”. Context awareness such as negation and pasts events were also applied. For example, the term “No chest pain”, “No known history of diabetes mellitus”, and “Symptoms of heart failure” will not be coded as the presenting condition. Additionally, terms like “Previous history of”, “Previous admission of”, and “Family history of” within the same sentence as a clinical condition will not be coded as the current condition for the record. 4. Discussion and Conclusion: When showcasing these abilities to the clinicians, the team agreed that MyHarmony was able to: (i) Generate more information from free-text utilising the SNOMED CT structure, thus, reducing the effort needed to collect data in a structured manner such as in a registry and indicator reports; (ii) Able to generate new information by retrospectively running new queries on old discharge summary records; thus, reducing the effort and time to collect data in a prospective manner when new questions arise, such as for indicator reports that often change on a yearly basis; (iii) Able to deliver information in a timelier fashion; thus clinicians and health managers are able to plan and take action without waiting for a 1 to 3 yearly report; (iv) Improve documentation of clinicians when they are aware of MyHarmony‟s ability during roadshows. Generating indicators for monitoring and evaluation can be a burden even for healthcare facilities equipped with EHR. Conventionally, collecting data for indicators requires multiple data entries in aggregated manner, with manual submission to central agencies, where the results are only published on a yearly basis. Introducing MyHarmony may reduce these burdens. Capturing data from the source in an automated way, i.e. free text documented by doctors, would reduce duplication of work and the amount of resources to capture the data into manual form. Having the data in granular form would allow a more
- 5. dynamic analysis and prevents dishonesty. Information required, whether old or new information, can be formulated and disseminated back to the clinicians and health managers in a timelier fashion. MyHarmony has the potential to expand further in its implementation and technology. However, there are still challenges to be addressed. Currently, MyHarmony has been developed to mine free-text for Cardiology via a back-end approach. It uses a single version of SNOMED CT International. The team is still researching the best approach to manage SNOMED CT versions and its codified data, which may impact the resulting analysis in an inconsistent way. The team are also seeking international experience for this matter. Other challenges include researching a more efficient and effective method to develop SNOMED CT Refsets. The initial method by referencing terms required by registries or indicators has been established. However, expanding the SNOMED CT Refsets to include relevant terms for a specific clinical specialty or domain needs to be refined further. Eye-balling technique to search the entire SNOMED CT content have its strength and weaknesses. Even though it is a very thorough method, there are possibilities of missed terms and very time consuming. Despite these challenges, the journey in developing MyHarmony and the lessons learnt has allowed the team to refine the methods and processes to expand the use of MyHarmony to other clinical specialties. Analysis from unstructured data would hope to complement analysis from structured data (like census and registries), with the additional benefit workload reduction to provide timelier, trusted, and dynamic information. References: 1. SNOMED CT Simple Reference Set: https://confluence.ihtsdotools.org/display/DOCRFSPG/5.1.+Simple+Reference+Set 2. Mohd Sulaiman I, Sheikh Ahmad MK. SNOMED CT Cardiology Reference Set Development, Malaysia. Proceedings of the SNOMED CT Implementation Showcase [Internet]. Amsterdam: IHTSDO; 2014. Retrieved from: http://ihtsdo.org/fileadmin/user_upload/doc/showcase/show14/SnomedCtShowcase2014_Abstract_14058 .pdf 3. Abdul Manaf NA, Mohamed K, Lukose D. Harmonizing EHR Databases with SNOMED CT. Proceedings of the SNOMED CT Implementation Showcase 2014 [Internet]. Amsterdam: IHTSDO; 2014. https://confluence.ihtsdotools.org/display/FT/SNOMED+CT+Implementation+Showcase+2014