Article Summary
The U.S. healthcare system was not prepared for a health crisis of the magnitude of the COVID-19 pandemic. Hospitals are working to facilitate widespread distribution of information within their organization and to local, state, and federal authorities to successfully manage this novel infection. EHRs and Lab Information Systems (LISs) have become public health tools for disease surveillance and management.
Due to signification variation in EHR data, informatics tools are needed to define patients with suspected SARS-Cov2 Infection and confirmed COVID-19 infection. With the aim of building an extensible model for a COVID-19 database, Health Catalyst has built a detailed approach that leverages a heuristic methodology for capturing both confirmed and suspected cases.
Health Catalyst has proposed value sets that define two patient cohorts for the registry for confirmed and suspected COVID-19 patients, stratified further into three levels of confidence: high confidence suspected, moderate confidence suspected, and low confidence suspected.
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Health Catalyst
Multiple Authors
Since the World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) as a pandemic on March 11, 2020, the United States has become the epicenter of the disease.1 While the numbers continue to increase, there are 4,710,282 confirmed cases1, surpassing the national case numbers of Spain, Italy, Germany, France, and China. This pandemic has highlighted the fact that the U.S. healthcare system is not prepared for a health crisis of this magnitude.2 EHRs and Lab Information Systems (LISs) have become public health tools for disease surveillance and management. Hospitals are working to facilitate widespread distribution of information within their organization and to local, state, and federal authorities to successfully manage this novel infection.
Additionally, reports of sparse testing kits, ventilators, and personal protective equipment (PPE) drive an urgent need for a means to calculate surge capacity requirements.3 EHR data has significant variation and informatics tools are needed to define patients with suspected SARS-Cov2 Infection and confirmed COVID-19 infection. With the aim of building an extensible model for a COVID-19 database, we have built a detailed approach that leverages a heuristic methodology for capturing both confirmed and suspected cases.
Based on the guidance provided by the CDC,4,5 WHO,6, 7 AMA,8 NLM VSAC,9 AAPC10 and published literature, we propose value sets that define two patient cohorts for the registry of confirmed and suspected COVID-19 patients, stratified further into three levels of confidence: high confidence suspected, moderate confidence suspected, and low confidence suspected. These value sets are based on ICD-10-CM codes (Supplemental Table 2) and will continue to evolve as we understand more about COVID-19, and as clinical practices change over time. Using the four patient cohorts for the registry defined using the value sets above, patient care outcomes can be studied.
Methods
We developed modified indicators that classify diagnosis codes into one of six value-sets (Diagnosis-Grain) and added additional logic on top of this to classify a patient into one of four diagnosis categories (Patient-Grain) intended to represent varying levels of diagnostic certainty. This was developed using Health Catalyst Touchstone® data with the intent to capture suspected C-19 patients not readily identifiable by lab tests and preceding widespread adoption of the ICD-10 code U07.1 for confirmed COVID-19, released for use on April 1, 2020.
Diagnosis-Grain:
1) Confirmed COVID-19
2) Viral exposure
3) Coronavirus-related
4) Associated COVID-19 Diagnoses
5) Severe Associated COVID-19 Diagnoses
6) COVID-19 Symptoms
Patient-Grain: [Figure 1]
1) Confirmed COVID-19
2) Suspected: High Confidence
3) Suspected: Moderate Confidence
4) Suspected: Low Confidence
Fig. 1. Proposed Use of Value Sets for Generating COVID-19 Patient Cohorts Using COVID-19 Categorization Logic
Key Differences Between CDC and Health Catalyst Value Sets:
Value Set: Confirmed COVID-19 Patients
The confirmed cohort removes ICD-10 Code B97.29 “Other coronavirus as the cause of diseases classified elsewhere” as this code captures all coronaviruses (including and in addition to COVID-19). People around the world are commonly infected with human coronaviruses 229E, NL63, OC43, and HKU1. There are additional human coronaviruses that are captured through the use of ICD-10 code B97.2. In our diagnosis logic, patients with this code are classified as “Suspected: High Confidence” if they do not have a positive non-COVID-19 coronavirus lab test.
| CDC Value-set: COVID-19 Confirmed | Health Catalyst Value-set: COVID-19 Confirmed | ||
|---|---|---|---|
| U07.1 | COVID-19 virus infection | U07.1 | COVID-19 virus infection |
| B97.29 | Other coronavirus as the cause of diseases classified elsewhere | Discuss U07.2 CODE (further discussion ongoing at HCAT) |
Value Set: Associated Diagnosis
Associated adds additional ICD-10 codes capturing the same or similar diagnoses as those documented in the CDC Guidelines, but extends beyond the specific ICD-10 codes documented in CDC Guidance. This is to account for varying usage of ICD-10 codes by clinicians across health systems. This value-set is intended to capture diagnoses characteristic of COVID-19 (moderate in severity, as mild symptoms and severe complications are captured in separate value-sets). Two codes have been moved to the Health Catalyst Severe Diagnosis value-set for purposes of use in the patient-grain diagnosis logic.
| CDC Value-set: COVID-19 Associated Diagnosis | Health Catalyst Value-set: COVID-19 Associated Diagnosis | ||
|---|---|---|---|
| A41.89 | Other specified sepsis | *A41.89 is moved to ‘severe’ value-set | |
| J06.9 | Acute upper respiratory infection, unspecified | ||
| J12.89 | Other viral pneumonia | J12.89 | Other viral pneumonia |
| J12.9 | Viral pneumonia, unspecified | ||
| J18.9 | Pneumonia, unspecified organism | ||
| J20.8 | Acute bronchitis due to other specified organisms | J20.8 | Acute bronchitis due to other specified organisms |
| J20.9 | Acute bronchitis, unspecified | ||
| J22 | Unspecified acute lower respiratory infection | J22 | Unspecified acute lower respiratory infection |
| J40 | Bronchitis, not specified as acute or chronic | J40 | Bronchitis, not specified as acute or chronic |
| J80 | Acute respiratory distress syndrome | *J80 is moved to ‘severe’ value-set | |
| J98.8 | Other specified respiratory disorders | J98.8 | Other specified respiratory disorders |
Value Set: Suspected COVID-19 Patients
Health Catalyst value-sets place the below codes in different value-sets for the purposes of allowing the patient-grain diagnosis logic to require additional elements to increase confidence that these codes are not representing non-C-19 coronaviruses. Clinically, these codes are intended to be used in conjunction with other diagnosis codes. Our value-sets do not include ICD-10 code Z03.818 “Encounter for observation for suspected exposure to other biological agents ruled out” as the guidance for this code is to be used to document cases ruled out.
People around the world are commonly infected with human coronaviruses 229E, NL63, OC43, and HKU1. Please see https://www.cdc.gov/coronavirus/types.html for additional human coronaviruses that are captured through use of ICD-10 codes B34.2 and B97.2.
| CDC Value-set: COVID-19 Suspected | Health Catalyst Value-set: COVID-19 Suspected | |
|---|---|---|
| B34.2 | Coronavirus infection, unspecified | *moved to ‘coronavirus related’ value-set |
| B97.2 | SARS-associated coronavirus as the cause of diseases classified elsewhere | *moved to ‘coronavirus related’ value-set |
| Z03.818 | Encounter for observation for suspected exposure to other biological agents ruled out | *Removed – indicates cases ruled out |
| Z20.828 | Contact with and (suspected) exposure to other viral communicable diseases* | *moved to ‘viral exposure’ value-set |
Value Sets: Coronavirus-Related and Viral Exposure
The codes in this value-set are found in the CDC Value-set ‘Suspected’ but are moved to their own value-sets for the purposes of the Patient-Grain logic.
| Health Catalyst Value-set: Viral Exposure | |
| Z20.828 | Contact with and (suspected) exposure to other viral communicable diseases (Not specific to COVID-19) |
| Health Catalyst Coronavirus-Related | |
| B34.2 | Coronavirus infection, unspecified (Not specific to C-19) |
| B97.21 | SARS-associated coronavirus as the cause of diseases classified elsewhere (Not specific to C-19) |
| B97.29 | Other coronavirus as the cause of diseases classified elsewhere (Not specific to C-19) |
Value Set: Severe Associated COVID-19 Diagnoses
This value-set is intended to be used in conjunction with other value-sets in the Patient-Grain Diagnosis Logic to help identify those patients who have developed severe symptoms/complications associated with C-19 likely requiring hospital-level care, in addition to other known C-19 symptoms and/or associated diagnoses. This is a compilation of some of the most commonly noted severe complications of COVID-19 noted in the literature.
| Health Catalyst: Severe Associated COVID-19 Diagnoses | |
|---|---|
| A41.89 | Other specified sepsis |
| A41.9 | Sepsis, unspecified organism |
| I50 | Heart failure |
| I50.1 | Left ventricular failure, unspecified |
| I50.20 | Unspecified systolic (congestive) heart failure |
| I50.21 | Acute systolic (congestive) heart failure |
| I50.23 | Acute on chronic systolic (congestive) heart failure |
| I50.3 | Diastolic (congestive) heart failure |
| I50.30 | Unspecified diastolic (congestive) heart failure |
| I50.31 | Acute diastolic (congestive) heart failure |
| I50.33 | Acute on chronic diastolic (congestive) heart failure |
| I50.40 | Unspecified combined systolic (congestive) and diastolic (congestive) heart failure |
| I50.41 | Acute combined systolic (congestive) and diastolic (congestive) heart failure |
| I50.42 | Chronic combined systolic (congestive) and diastolic (congestive) heart failure |
| I50.43 | Acute on chronic combined systolic (congestive) and diastolic (congestive) heart failure |
| I50.810 | Right heart failure, unspecified |
| I50.811 | Acute right heart failure |
| I50.813 | Acute on chronic right heart failure |
| I50.814 | Right heart failure due to left heart failure |
| I50.82 | Biventricular heart failure |
| I50.83 | High output heart failure |
| I50.89 | Other heart failure |
| I50.9 | Heart failure, unspecified |
| I51.3 | Intracardiac thrombosis, not elsewhere classified |
| I51.4 | Myocarditis, unspecified |
| I51.5 | Myocardial degeneration |
| I51.9 | Heart disease, unspecified |
| J80 | Acute respiratory distress syndrome |
| R06.03 | Acute respiratory distress |
| R65.11 | Systemic inflammatory response syndrome (SIRS) of non-infectious origin with acute organ dysfunction |
| R65.20 | Systemic inflammatory response syndrome (SIRS) of non-infectious origin with acute organ dysfunction, without septic shock |
| R65.21 | Systemic inflammatory response syndrome (SIRS) of non-infectious origin with acute organ dysfunction, with septic shock |
Value Set: COVID-19 Symptoms
This value-set is intended to be used in conjunction with other value-sets in the Patient-Grain Diagnosis Logic to help identify those patients who have symptoms of C-19 in addition to other known C-19 severe complications and/or associated diagnoses. This is a compilation of some of the most commonly noted symptoms and emerging symptoms of COVID-19 noted in the literature.
| Health Catalyst Value-set: COVID-19 Symptoms | |
|---|---|
| H10 | Conjunctivitis |
| H10.011 | Acute follicular conjunctivitis, right eye |
| H10.012 | Acute follicular conjunctivitis, left eye |
| H10.013 | Acute follicular conjunctivitis, bilateral |
| H10.019 | Acute follicular conjunctivitis, unspecified eye |
| H10.021 | Other mucopurulent conjunctivitis, right eye |
| H10.022 | Other mucopurulent conjunctivitis, left eye |
| H10.023 | Other mucopurulent conjunctivitis, bilateral |
| H10.029 | Other mucopurulent conjunctivitis, unspecified eye |
| H10.231 | Serous conjunctivitis, except viral, right eye |
| H10.232 | Serous conjunctivitis, except viral, left eye |
| H10.233 | Serous conjunctivitis, except viral, bilateral |
| H10.239 | Serous conjunctivitis, except viral, unspecified eye |
| H10.30 | Unspecified acute conjunctivitis, unspecified eye |
| H10.31 | Unspecified acute conjunctivitis, right eye |
| H10.32 | Unspecified acute conjunctivitis, left eye |
| H10.33 | Unspecified acute conjunctivitis, bilateral |
| H10.89 | Other conjunctivitis |
| H10.9 | Unspecified conjunctivitis |
| M79.10 | Myalgia, unspecified site |
| R05 | Cough |
| R06.0 | Dyspnea |
| R06.00 | Dyspnea, unspecified |
| R06.01 | Orthopnea |
| R06.02 | Shortness of breath |
| R06.03 | Acute respiratory distress |
| R06.09 | Other forms of dyspnea |
| R07.0 | Pain in throat |
| R07.1 | Chest pain on breathing |
| R07.2 | Precordial pain |
| R07.8 | Other chest pain |
| R07.81 | Pleurodynia |
| R07.82 | Intercostal pain |
| R07.89 | Other chest pain |
| R07.9 | Chest pain, unspecified |
| R43.0 | Anosmia |
| R43.1 | Parosmia |
| R43.2 | Parageusia |
| R43.9 | Unspecified disturbances of smell and taste |
| R50 | Fever of other and unknown origin |
| R50.81 | Fever presenting with conditions classified elsewhere |
| R50.9 | Fever, unspecified |
| R51 | Headache |
| R53 | Malaise and fatigue |
Lab Test Knowledge Curation
Due in part to the critical diagnostic importance of lab testing, lab test result data are key to understanding the clinical state of patients as well as surveillance of patient populations. However, in EHR systems lab test result data are often stored with local codes or strings for lab test types and test result values rather than codes from widely standardized terminologies such as LOINC (Logical Observation Identifiers Names and Codes).19 In such cases, there is a need to ascertain the types of the lab tests and the meanings of the result values automatically over a large volume of lab result data without the benefit of a uniform standard lab terminology across the various EHR systems from which the lab result data are sourced.
In this work, a lab test knowledge curation workflow (fig. 2) was established in order to provide a knowledge base for recognizing lab test types and understanding lab result values as expressed in the lab result data records from multiple EHR systems. This lab test knowledge curation workflow is shown in the following diagram. This knowledge base, accumulated from many lab result records across multiple EHR systems, allows for automated categorization of lab results (positive, negative, pending, ambiguous, test problem, unmapped) and lab test types (detection of SARS-CoV-2 material, detection of antibody to SARS-CoV-2). This classification system is of great convenience for use in analytics to find patterns of interest in, for example, the data of patients who are confirmed or suspected COVID-19 cases. The harmonized set of lab result values allows for rapid automated use of readily comprehensible data (i.e., positive and negative result values), identification of results that may be usable with additional data curation (i.e., unmapped), and the ability to select for patients whose tests may still be in process or unavailable (i.e., pending).
Figure 2. Laboratory Test Knowledge Curation Workflow
References
- Johns Hopkins Coronavirus Resource Center. COVID-19 Map [Internet]. [cited 2020 May18].
- Lipsitch M, Swerdlow DL, Finelli L. Defining the Epidemiology of Covid-19 – Studies Needed. N Engl J Med. 2020 Mar 26;382(13):1194-1196. doi:10.1056/NEJMp2002125.
- Hospital Experiences Responding to the COVID-19 Pandemic: Results of a National Pulse Survey March 23–27, 2020. U.S. Department of Health and Human Services Office of Inspector General. [cited 2020 Apr 10].
- Consortium for Clinical Characterization of COVID-19 by EHR. [cited 2020 May 21].
- The Centers for Disease Control and Prevention. Human Infection with 2019 Novel Coronavirus Person Under Investigation (PUI) and Case Report Form. [cited 2020 Apr 10].
- World Health Organization. Emergency Use ICD Codes for COVID-19 disease outbreak. [cited 2020 May 21].
- COVID-19 coding in ICD-10. World Health Organization. [cited 2020 May 21].
- American Medical Association. COVID-19 coding and guidance. [cited 2020 May 21].
- National Library of Medicine. COVID-19 Value Sets in VSAC. [cited 2020 May 21].
- American Academy of Professional Coders. COVID-19: Your Medical Coding and Compliance Headquarters [cited 2020 May 21].
Additional Reading
Would you like to learn more about this topic? Here are some articles we suggest:
- Steps for Effective Patient and Staff Contact Tracing to Defend Against COVID-19 Spread
- How to Scale Telehealth Solutions to Increase Patient Access During COVID-19
- Activity-Based Costing in Healthcare During COVID-19: Meeting Four Critical Needs
- What Health Systems Need to Know About COVID-19 Relief Funding
- Cross-State Nurse Licensing: One Way to Improve Care During COVID-19
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