Terminology Standards for Health Information Exchange in the times of SARS-Cov2

To provide better and cost effective patient care, one needs to exchange healthcare information. For this to happen seamlessly, there is a dire need of Standards that facilitate this interoperability.

A Standard denotes the ability of two or more systems or components to exchange information (structural or syntactic interoperability) and to (meaningfully) use the information that has been exchanged (functional or semantic interoperability).

The EHR Standards, 2^nd edition, were notified by the Ministry of Health and Family Welfare, Government of India (MoHFW) in December 2016.

Subsequently, to give a boost to implementation of digital health in India, the National Digital Health Blueprint (NDHB) has been finally notified, and it also mentions a minimal set of standards to be used. It tries to define the standards required for ensuring interoperability within the National Digital Health Eco-system.

Also, now the Telemedicine Practice Guidelines have been notified by the MoHFW and NITI Aayog.

Categories of Standards for exchange of Health Information

The broad categories for Standards mentioned in the NDHB are those for Consent, (Clinical) Content, Privacy and Security, Patient Safety and Data Quality.

Currently for epidemiological purposes, all countries send reports to the WHO using the ICD classification system (current version is ICD-10, while ICD-11 has been formally released last year and will be applicable from January 2022). However, for getting better insights into the clinical data, SNOMED CT (a clinical terminology system) is the globally preferred standard and India has been a country member of SNOMED International since 2014. The basic differences of these two systems are summarized below.

•     ICD (International Statistical Classification of Diseases) codes, from the WHO, have limited scope and granularity, summarizes and aggregates data into broad categories (for epidemiological purposes), and are mono-hierarchical (Each code is grouped into a single grouping) –

•        No links to body sites or causes

•        Groups multiple clinical meanings  together using a single code

•        Does not always represent sufficient detail for clinical purposes

•      SNOMED CT is broader in scope, more granular, allows data to be grouped and aggregated in different ways (poly-hierarchical), and to be queried, based on Relationships between the Concepts. Also, since it is inherently logical, developing Clinical Decision Support Systems (CDSS) is also relatively easier with SNOMED-CT enabled systems.

Presently mappings are available from to SNOMED CT to ICD-10 and its various adaptations. Therefore, if any system is SNOMED CT enabled, it is possible to report according to ICD-10 or 11 as may be the statutory requirement for epidemiological and public health purposes.

Updating for SARS-Cov2

Now, with the world being gripped by a new Pandemic, the SDOs (Standards Development Organizations) have also geared up and come up with pertinent standards for this novel Corona virus or Covid-19 (Corona virus Disease 2019) or SARS-Cov2 (Severe Acute Respiratory Syndrome Coronavirus-2). The World Health Organization (WHO) has named the syndrome caused by this coronavirus “COVID-19”, and the International Committee on Taxonomy of Viruses (ICTV) has named the virus SARS-CoV-2.

The COVID-19 disease outbreak has been declared a public health emergency of international concern. The WHO has included it into the ICD system:

o    An emergency ICD-10 code of ‘U07.1 COVID-19, virus identified’ is assigned to a disease diagnosis of COVID-19 confirmed by laboratory testing.

o    An emergency ICD-10 code of ‘U07.2 COVID-19, virus not identified’ is assigned to a clinical or epidemiological diagnosis of COVID-19 where laboratory confirmation is inconclusive or not available.

o    Both U07.1 and U07.2 may be used for mortality coding as cause of death

o    In ICD-11, the code for the confirmed diagnosis of COVID-19 is RA01.0 and the code for the clinical diagnosis (suspected or probable) of COVID-19 is RA01.1.

A more detailed breakup for ICD-10 is available at: https://www.who.int/classifications/icd/COVID-19-coding-icd10.pdf?ua=1

SNOMED International has come out by placing the concept under the parent Human Coronavirus (Organism): Severe acute respiratory syndrome coronavirus 2 (organism) – SCTID: 840533007

840533007 | Severe acute respiratory syndrome coronavirus 2 (organism) |

  en   Severe acute respiratory syndrome coronavirus 2 (organism)
  en   2019-nCoV
  en   Severe acute respiratory syndrome coronavirus 2
  en   SARS-CoV-2
  en   2019 novel coronavirus

And, under Coronavirus infection (Disorder): Disease caused by severe acute respiratory syndrome coronavirus 2 (disorder) – SCTID: 840539006

840539006 | Disease caused by severe acute respiratory syndrome coronavirus 2 (disorder) |

  en   Disease caused by severe acute respiratory syndrome coronavirus 2
  en   COVID-19
  en   Disease caused by 2019 novel coronavirus
  en   Disease caused by 2019-nCoV
  en   Disease caused by severe acute respiratory syndrome coronavirus 2 (disorder)

The Regenstrief Institute that develops the LOINC codes, is developing Special Use codes in response to an urgent or emergent situation. These codes are based on the most up to date information available at the time of their creation. They have undergone the normal QA terminology process. LOINC supports their use in the unique situation that resulted in their rapid creation. However, be aware that downstream users may not be ready to handle prerelease codes until they are published in an official release. The emerging codes for Covid-19 are available at: https://loinc.org/sars-coronavirus-2/

Conclusion

The pandemic of SARS-Cov2 is evolving, and, so are the Standards related to the exchange of health information because of the disorder and / or organism. Once the situation stabilizes a bit, the unambiguity in the semantic exchange of such information will also become clear.

References

1. .      National Health Portal, Ministry of Health and Family Welfare, Government of India, EHR Standards. Available from: https://www.nhp.gov.in/ehr-standards-helpdesk_ms
2. .      Ministry of Health and Family Welfare, Government of India, National Digital Health Blueprint, 2019, Available from: https://main.mohfw.gov.in/sites/default/files/Final%20NDHB%20report_0.pdf (A compressed version is available at: https://main.mohfw.gov.in/sites/default/files/Final%20Report%20-%20Lite%20Version.pdf )
3. .      Ministry of Health and Family Welfare, Government of India, Telemedicine Practice Guidelines: https://www.mohfw.gov.in/pdf/Telemedicine.pdf
4. .      Sarbadhikari SN, The Role of Standards for Digital Health and Health Information Management, JBCR, 2019, 6(1):1: https://jbcr.net.in/JBCR-VOL-6-issue-1-2019-20/current-issues-volume-VI-issue-1-1.html

5. Sarbadhikari SN, Digital Health in India - as envisaged by the National Health Policy (2017), Guest Editorial, BLDE University Journal of Health Sciences, 2019, 4: 1-6.
6. SNOMED International, SNOMED CT Basics: https://confluence.ihtsdotools.org/display/DOCSTART/4.+SNOMED+CT+Basics
7. WHO, ICD-10: https://www.who.int/classifications/icd/covid19/en/
8. SNOMED International: http://www.snomed.org/news-and-events/articles/snomed-loinc-coronavirus-collaboration
9. SNOMED International: https://browser.ihtsdotools.org/
10. Regenstrief Institute, LOINC codes: https://loinc.org/prerelease/

Health Data Analytics and clinical terminology systems like SNOMED CT

Healthcare, with its inherent complexity, deals with large volumes of data coming in. Well designed and used EMRs (Electronic Medical Records) can collect huge amounts of data. However, neither the volume nor the velocity of data in traditional modern healthcare may qualify as big data now. Only a small fraction of the tables in an EMR database may be relevant to the current practice of medicine and its corresponding analytics use cases.

Certainly there will be variety in the data, but most of the EMR systems collect very similar data objects and models. However, new use cases supporting genomics and Internet of Medical Things (IoMT) will certainly require a big data approach.

Healthcare (data) analytics describes healthcare analysis activities that can be undertaken as a result of data collected from four areas within healthcare:

• Claims and cost data
• Pharmaceutical and research and development (R&D) data
• Clinical data (collected from electronic medical records (EMRs))
• Patient behavior and sentiment data (patient behaviors and preferences)

In health information management (HIM) — and in coding, specifically — the HIM professional must understand the importance of their role in interpreting and abstracting the data to be collected and analyzed. In other words, (health) data literacy is essential. While this data is used primarily in reimbursement and claims activities, it also plays a much larger role in clinical data analysis performed in facilities for quality of care reporting, disease management, and best care practices.

HIM Professionals are implementing coding data analytics to continually monitor their coding teams and cost-justify ongoing educational investments. Coding data analytics is a long-term commitment to improve coding performance for productivity and accuracy.

Elements that impact coding productivity data include: the type of electronic health record (EHR) used, the number of systems accessed during the coding process, clinical documentation improvement (CDI) initiatives, turnaround time (TAT) for physician queries, and the volume of non-coding tasks assigned to coding teams.

Accuracy should never be compromised for productivity. That may lead to denied claims, payer scrutiny, reimbursement issues, and other negative financial impacts. Instead, a careful balance between coding productivity and accuracy is considered best practice. Both data sets must be assessed simultaneously. The most common way to collect coding accuracy data is through coding audits and a thorough analysis of coding denials.

The fully-electronic 11th edition of the International Statistical Classification of Diseases (ICD-11) from World Health Organization (WHO) contains (epidemiological or morbidity and mortality causes) 55,000 codes, compared to the 14,400 in ICD-10.

SNOMED CT (India is a member of SNOMED CT and therefore it is available for use by anyone in India, free of cost) from SNOMED International contains 311,000 clinical concepts (including anatomical sites, disease diagnosis and procedures), with their descriptions, and more importantly, poly-hierarchical relationships.

To quote SNOMED CT:
"SNOMED CT is a clinically validated, semantically rich, controlled terminology designed to enable effective representation of clinical information. SNOMED CT is widely recognized as the leading global clinical terminology for use in Electronic Health Records (EHRs). SNOMED CT enables the full benefits of EHRs to be achieved by supporting both clinical data capture, and the effective retrieval and reuse of clinical information.

The term 'analytics' is used to describe the discovery of meaningful information from healthcare data. Analytics may be used to describe, predict or improve clinical and business performance, and to recommend action or guide decision making.

Using SNOMED CT to support analytics services can enable a range of benefits, including:

• Enhancing the care of individual patients by supporting:
• Retrieval of appropriate information for clinical care
• Guideline and decision support integration
• Retrospective searches for patterns requiring follow-up
• Enhancing the care of populations by supporting:
• Epidemiology monitoring and reporting
• Research into the causes and management of diseases
• Identification of patient groups for clinical research or specialized healthcare programs
• Providing cost-effective delivery of care by supporting:
• Guidelines to minimize risk of costly errors
• Reducing duplication of investigations and interventions
• Auditing the delivery of clinical services
• Planning service delivery based on emerging health trends

SNOMED CT has a number of features, which makes it uniquely capable of supporting a range of powerful analytics functions. These features enable clinical records to be queried by:

• Grouping detailed clinical concepts together into broader categories (at various levels of detail);
• Using the formal meaning of the clinical information recorded;
• Testing for membership of predefined subsets of clinical concepts; and
• Using terms from the clinician's local dialect.

SNOMED CT also enables:

• Clinical queries over heterogeneous data (using SNOMED CT as a common reference terminology to which different code systems can be mapped);
• Analysis of patient records containing no original SNOMED CT content (e.g. free text);
• Powerful logic-based inferencing using Description Logic reasoners;
• Linking clinical concepts recorded in a health record to clinical guidelines and rules for clinical decision support; and
• Mapping to classifications, such as ICD-9 or ICD-10, to utilize the additional features that these provide.

Analytics tasks, which may be enabled or enhanced by the use of SNOMED CT techniques, can be considered in three broad categories:

1. Point-of-care analytics, which benefits individual patients and clinicians. This includes historical summaries, decision support and reporting.
2. Population-based analytics, which benefits populations. This includes trend analysis, public health surveillance, pharmacovigilance, care delivery audits and healthcare service planning, and
3. Clinical research, which is used to improve clinical assessment and treatment guidelines. This includes identification of clinical trial candidates, predictive medicine and semantic searching of clinical knowledge. 

While the use of SNOMED CT for analytics does not dictate a particular data architecture, there are a few key options to consider, including:

• Analytics directly over patient records;
• Analytics over data exported to a data warehouse;
• Analytics over a Virtual Health Record (VHR);
• Analytics using distributed storage and processing; and
• A combination of the above approaches.

Practically all analytical processes are driven by database queries. To get the most benefit from using SNOMED CT in patient records, record-based queries and terminology-based queries must work together to perform integrated queries over SNOMED CT enabled data. To this end, SNOMED International is developing a consistent family of languages to support a variety of ways in which SNOMED CT is used. Clinical user interfaces can also be designed to harness the capabilities of SNOMED CT, and to make powerful clinical querying more accessible. Innovative data visualization and analysis tools are becoming more widespread as the capabilities of SNOMED CT content are increasingly utilized."

Therefore, now it is possible to make every kind of analytics and reporting results much more detailed than it used to be. Billing and coding companies that have adopted predictive analysis tools have received a considerably higher value return from mining their data.

HIM professionals must encourage the administration and policymakers to adopt SNOMED-CT enabled systems to get better informed and analyzed outcomes.

References:

1. https://www.healthcatalyst.com/big-data-in-healthcare-made-simple
2. https://www.healthcareittoday.com/2017/11/15/opening-the-door-to-data-analytics-in-medical-coding-him-scene/
3. https://www.osplabs.com/insights/data-mining-in-medical-coding-and-billing/
4. https://bok.ahima.org/doc?oid=302591#.Xj-2xvkzbDc
5. https://confluence.ihtsdotools.org/display/DOCANLYT/Data+Analytics+with+SNOMED+CT
6. https://confluence.ihtsdotools.org/display/DOC
7. https://jbcr.net.in/JBCR-VOL-6-issue-1-2019-20/current-issues-volume-VI-issue-1-1.html
8. https://www.healthcatalyst.com/the-case-for-healthcare-data-literacy
9. https://nnlm.gov/data/guides/data-literacy/course-materials
10. https://confluence.ihtsdotools.org/display/DOCANLYT/1+Executive+Summary