An Experience of Independent Evaluation of Final MBBS Examinations

I was recently invited by Prof. Rakesh Biswas, to provide a blinded assessment of 33 students who just appeared for their final MBBS examinations at the Department of General Medicine, in Kamineni Institute of Medical Sciences, by assessing their long case, short case and seeing their performance recorded on video. 

This was quite an illuminating experience for me. For the concerns of privacy, I would not share any identifiable personal details. However, I can share my overall impressions.

The Ugly

Plagiarism is a worry. In some of the cases, the students seemed to be totally clueless. Apparently they hardly attended any clinics. Or were the clinics too pedantic and boring?

The Bad

Currently the MBBS curriculum is ignoring a fact that medical knowledge is doubling in about 70 days. It is just not possible for anyone to remember everything that is up to date. Nevertheless, the basics remain the same. 

While modern technological advancements do provide us greater insights, it also makes us helpless where none of those are available and only clinical acumen can help. Clinical acumen cannot be built in a day, but sincere repeated efforts are necessary. In the process of getting used to MCQs, the obvious signs often bypass us. Moreover the eye does not see what the mind does not know and none so blind as will not see. These are not useless adages but rather axiomatic.

Making a list of Provisional Diagnosis judiciously requires skills of health informatics, especially health data literacy. Those are beyond the scope of the NMC prescribed essential curriculum.

The Good

In general, most of the students were methodical in history taking and recording them properly. As expected, some of the students appeared as sincere learners.

 

 

 

All in all, it was a privilege and honor to have got the opportunity to have re-evaluated the students, though remotely. In essence, it appeared that the medical curriculum, especially the learning methodologies needs a thorough revamping. The obvious question now is whether AI will replace doctors? The answer is simple. Doctors not using AI will be inevitably replaced by doctors using AI because Brain plus Computer is always greater than either alone.

My publications and conference presentations in 2022

 

Publications in 2022

1.        John O, Sarbadhikari SN, Prabhu T, Goel A, Thomas A, Shroff S, Allaudin F, Weerbaaddana C, Alhuwail D, Koirala U, Johnrose J, Codyre P, Bleaden A, Singh S, Bajaj S, Implementation Experiences of Telehealth balancing Policies with Practice in some countries of South Asia, Kuwait and the European Union, Interact J Med Res 2022;11(1):e30755 DOI: 10.2196/30755 http://dx.doi.org/10.2196/30755 URL: https://www.i-jmr.org/2022/1/e30755
2.        Gudi N, Kamath P, Chakraborty T, Jacob AG, Parsekar SS, Sarbadhikari SN, John O, Regulatory Frameworks for Clinical Trial Data Sharing: Scoping Review, J Med Internet Res, 2022;24(5):e33591, doi: 10.2196/33591
3.   Book Chapter: Sarbadhikari SN & Srinivas M, Health Informatics and Health Information Management, In, Gyani G & Thomas A, Eds, Handbook of Healthcare Quality and Patient Safety, Jaypee, New Delhi, 3^rd ed, 2022, Sec. 4 (Clinical Governance), Ch. 20: 274-286.
4.       Book Chapter: Sarbadhikari SN, Priyadarshini B, Kutikuppala LVS, Jodavula S, Mukherjee S, Krishna GV, Sharma S, Price A and Biswas R, Scholarship of Integration and the future of Medical Education and Research (MER), Ch. 29, In, Adkoli BV and Ray A, Eds, Medical Education Research: Theory, Practice, Publication and Scholarship, Notion Press, Chennai, India, 2022: 353-368.
5.       Book Chapter: Sarbadhikari SN, Chapter 5: Career Prospects in Medical Technology and Research, In, Thomas, A., Raghunath, S., Alexander, D., Bhalla, S. Eds, Technology and the Health Sector, Indus Publishers: Chennai, 2022 [In Press] 

 

Conference Presentations in 2022

 

1.  Sarbadhikari SN, Resource person at 21^st National level Medical Record Conference (MEDRECON-2022), AIIMS Raipur (CG) on 11-12 March, 2022.
2.   Sarbadhikari SN, Resource person at the International Workshop on Health Informatics Curriculum Development, MGUMST, Jaipur on 25-27 March 2022.
3.     Sarbadhikari SN, panel member at its inaugural Health-Tech Masterclass on the Role of Hospital Technology in Patient Experience Management in Eastern India, held on 8^th June, 2022 at Altair Boutique, Kolkata
4.      Sarbadhikari SN, Keynote Speaker on “Digital Public Health” at 62^nd WB IPHACON 2022, 25-26 June 2022, The Stadel, Kolkata
5.    Sarbadhikari SN, Resource person, Eastern India Digital Health Conclave: Opportunities and Challenges, Theme: Digitalization of Healthcare: The Need of the Hour, 6^th August 2022, NASSCOM Initiative, Kolkata

 

 

Digital Health in India on 2022

Welcome to a New Year 2022. I am joining as Professor and Head, Department of Digital Health Research at Santiniketan Digital Research Centre, attached to the Santiniketan Medical College, Bolpur, West Bengal, India. This has been a long journey since I had been involved with the Centre for Digital Health in Amrita Hospitals, Kochi, There we had started the first MSc (Medical Informatics) course in India from 2005. However, as it was way ahead of the times, it had to be discontinued by three years.

Now, with India embracing the Ayushman Bharat Digital Mission (ABDM), which aims to develop the backbone necessary to support the nationally integrated digital health infrastructure, it is expected to bridge the existing gap amongst different stakeholders of Healthcare ecosystem through digital highways. The National Resource Centre for EHR standards (NRCeS) at C-DAC, Pune is working very hard to offer practical solutions to the teething problems of adopting EHR Standards and the National Digital Health Blueprint (NDHB). These are all the manifestations of the third edition of the National Health Policy (2017). Apart from the EHR Standards recommended by MoHFW (2nd ed, 2016), NDHB had also recommended HL7 FHIR R4. The FHIR (India) Profiles for ABDM Health Data Interchange Specifications, which are being developed by a very active FHIR India community, are being vetted and maintained by NRCeS.

Another encouraging development has been the enactment of National Commission for Allied and Healthcare. Professions (NCAHP) Act, 2021. Under the National Commission there will be ten National Councils of which the tenth one is the National Council for Health Information Management and Health Informatic Professionals.  Prior to this the erstwhile Medical Council of India had proposed the Competency based Medical Education (CBME) for the Indian Medical Graduate. There one of the goals (2.3.2) of the learner is to be a leader and member of the healthcare team and system with capabilities to collect, analyze, synthesize and communicate health data appropriately.

Therefore, I would like to welcome 2022 as a landmark year that will usher digital health in India in a big way.

SNOMED CT and Nursing

 Introduction

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 ^1,2. 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). For semantic interoperability and getting better insights into clinical data, SNOMED CT is preferred.

SNOMED CT, is accepted as the global common language for clinical terms, otherwise known as a Clinical Terminology System. SNOMED CT based clinical information benefits individual patients and clinicians as well as populations while supporting evidence-based care. 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. Therefore, SNOMED-CT enabled EHR systems can lead to better informed and analyzed patient outcomes.

Earlier Efforts

Earlier, the use of a common list of SNOMED CT concepts was thought to maximize data interoperability among institutions. However, local problem list vocabularies often needed to be expanded to satisfy specific user needs. Users had to check to see if SNOMED CT contains terminology for concepts they need to fulfill their local requirements and use case. The UMLS Terminology Services (UTS) from the National Library of Medicine (NLM) ³, USA, includes a SNOMED CT browser that may be used for this purpose. The SNOMED CT Browser is available through the SNOMED CT menu of the UTS.

If needed concepts are not found in the terminology, users are encouraged to submit them for inclusion in the US Extension of SNOMED CT. To submit a new nursing problem concept, one can go to U.S. SNOMED CT Content Request System (USCRS). Additional assistance on submitting requests can be found on the USCRS User Guide page. This is how the link to the Nursing Problem List concepts is maintained and divergence of problem list vocabularies can be minimized. Institutions that use their own problem list vocabularies are encouraged to map them to SNOMED CT with a focus on the Nursing Problem List concepts to facilitate data interoperability.

Recent Happenings

The 2020 agreement ⁴ between SNOMED International and the International Council of Nurses (ICN) highlights the clinical work of nursing. The ICN is a federation of more than 130 national nurses’ associations worldwide. Under the agreement, the International Classification for Nursing Practice (ICNP) will be incorporated into SNOMED CT, which has more than 350,000 active concepts, and SNOMED International will manage, produce and release the ICNP on behalf of ICN, which will maintain governance and ownership of the ICNP content.

National and international stakeholders that use SNOMED CT can seamlessly integrate ICNP concepts into their SNOMED CT-enabled EHRs or electronic health records. The countries that presently use both ICNP and SNOMED CT will no longer need to engage in mapping activities between these two code systems. The agreement coincides with the World Health Organization (WHO) declaring 2020 as the Year of the Nurse and Midwife, and it also builds on SNOMED International’s commitment to global interoperability as part of the Joint Initiative Council for Global Health Informatics Standardization.

This collaboration has advanced interoperability, and also supported disease surveillance in real time. ICNP concepts represent the practice of 20 million nursing professionals worldwide. Interoperability is more than just health information systems exchanging information. It also means that EHR systems can exchange data with unambiguous, shared meaning and that the clinicians who document and interpret those concepts can then understand and utilize them in the same way across practices and clinical settings, transcending geographical and time barriers.

With ICNP, one can measure nursing-related indicators; then use that information to improve global, national and regional health care systems; and use that data to produce scientific evidence that will inform health system transformation for enabling global health.

COVID-19 pandemic has renewed the international focus on public health infrastructure and measures, and globally health care agencies are re-investing in community-based nursing activities including prevention, surveillance and immunization. The relationship between ICNP and SNOMED CT makes it possible for reporting of COVID-19 public health surveillance at regional, national and international levels. As nurses do the bulk of sample collection, this partnership could enable public health officials to have access to near real-time testing data that has been reliably documented by regulated health professionals, as well as subsequent additional analyses about nursing care. More significantly, the improvements in patient outcomes and the ability to meet the health needs of patients are possible through integrated health and social care systems.

Conclusions and Way Forward

While efforts are ongoing to ensure adequate coverage of nursing in SNOMED CT, there have been no studies indicating the use of SNOMED CT in nursing practice. The authors ⁵ recommend for achieving the widespread collection of interoperable SNOMED CT coded nursing data in clinical applications to evaluate nursing’s impact on patient outcomes. This is the right time to encourage the advocacy and use of SNOMED CT for enhancing the quality of nursing services.

References

1.    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

2.    Sarbadhikari SN, Health Data Analytics and clinical terminology systems like SNOMED CT, https://supten.blogspot.com/2020/03/terminology-standards-for-health.html

3.    National Library of Medicine, Nursing Problem List Subset of SNOMED CT, https://www.nlm.nih.gov/research/umls/Snomed/nursing_problemlist_subset.html

4.    Davison K and Kuru K, Integration of ICNP into SNOMED CT brings voice to nursing practice, https://www.snomed.org/news-and-events/articles/integration-ICNP-SNOMEDCT-brings-voice-to-nursing

5.    Junglyun Kim, Tamara G.R. Macieira, Sarah L. Meyer, Margaret Ansell (Maggie), Ragnhildur I. Bjarnadottir (Raga), Madison B. Smith, Sandra Wolfe Citty, Denise M. Schentrup, Rose Marie Nealis, Gail M. Keenan, Towards implementing SNOMED CT in nursing practice: A scoping review, International Journal of Medical Informatics, 2020, 134: 103045, https://doi.org/10.1016/j.ijmedinf.2019.104035

Standards for (Human) Brain Machine (Computer) Interface

 

A brain‐machine interface (BMI), also known as a Human-Computer Interface (HCI), is a system that establishes a direct communication channel between the human (or animal) brain and a computer or an external device. BMIs record or stimulate activity of the central (CNS) or peripheral nervous system (PNS) to replace, restore, enhance, supplement, or improve natural output/input. Thereby the BMI is able to change the ongoing interactions between the CNS and its external or internal environment. BMIs usually measure neural activity through sensors placed inside the brain or body (invasive or implanted technologies) or external sensors (non‐invasive technologies). This activity is processed in real‐time to extract information about the intentions or states of the subject, and then generate an action or stimulus in the external world that is provided as direct or indirect feedback to the user.

BMI systems are the product of integrating multiple technologies. They consist of systems for the acquisition and decoding of neural and biophysical signals to actuators providing sensory, mechanical, and electrical feedback to the user.

Ethical and social / legal concerns are being addressed by related IEEE guidelines for ethically aligned‐Intelligent systems, and the IEEE Brain Neuroethics framework.

BMI standardization should also consider regulatory frameworks for technology‐based systems (both clinical and consumer oriented) in fields like AI, IoT, and cybersecurity. A particular challenge in this aspect is the global disparity in regulatory approaches.

Standards for neurotechnologies

Currently, sensing and actuation technologies (e.g., IEEE 21451-1-2010 - ISO/IEC/IEEE Standard for Information technology -- Smart transducer interface for sensors and actuators -- Part 1: Network Capable Application Processor (NCAP) information model) are well standardized. These standards are mainly focused on safety aspects of those technologies. Data management Standards (e.g., various Standards from ISO / JTC 1/SC 32) cover aspects such as cybersecurity and data representation in medical applications.

On the other hand, system‐level aspects of BMI such as user needs and performance assessment require standards. Although there are existing standards regarding human factors and usability, they are not widely applied by the BMI research and development community.

Remarkably, recent advances in the field and the prospect of commercialization of both clinical and consumer‐oriented applications have motivated multiple efforts to develop guidelines and standards. An important milestone is the release of the FDA draft guideline on implanted brain‐computer interfaces in spring 2019.

To bring forth concerted efforts of Standardization, IEEE Working Group P2731 has recently been established to create a standard for Unified Terminology for Brain‐Computer Interfaces, while IEEE Working Group P2794 is working to formulate a Reporting Standard for in vivo Neural Interface Research (RSNIR), to serve as a framework for the precise, comprehensive reporting of human and animal research throughout the growing ecosystem of neurotechnology.

 

References

1.    K. Bowsher et al., “Brain‐computer interface devices for patients with paralysis and amputation: a meeting report.,” J Neural Eng, vol. 13, no. 2, p. 23001, Feb. 2016.

2.    Food and Drug Administration (FDA), “Implanted Brain‐Computer Interface Devices for Patients With Paralysis or Amputation—Nonclinical Testing and Clinical Considerations.” 2019.

3.    IEEE SA, Standards Roadmap: Neurotechnologies for Brain-Machine Interfacing, IEEE SA Industry Connections Activity No. IC17-007,New York, USA, 2020.

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