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Using COVID-19 Value Sets for Patient Identification
Sadiqa Mahmood, DDS, MPH
General Manager & Senior Vice President, Life Sciences Business
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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Since theWorld 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.1While the numbers continue to increase, there are 4,710,282 confirmed cases1超过了西班牙、意大利、德国、法国和中国的全国病例数量。这次大流行凸显了一个事实，那就是美国的医疗系统并没有准备好应对如此严重的健康危机。2电子病历和实验室信息系统(LISs)已成为疾病监测和管理的公共卫生工具。医院正在努力促进信息在其组织内的广泛传播，并向地方、州和联邦当局成功地管理这种新型感染。
此外，有关检测试剂盒、呼吸机和个人防护设备(PPE)稀疏的报告促使迫切需要一种计算超驰能力需求的方法。3EHR 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,4,5WHO,6, 7AMA,8NLM VSAC,9AAPC10and 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 nothave a positive non-COVID-19 coronavirus lab test.
| CDC Value-set: COVID-19 Confirmed | 世界杯葡萄牙vs加纳即时走地健康催化剂值集: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
Associatedadds 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 | 世界杯葡萄牙vs加纳即时走地健康催化剂值集: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 includeICD-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 casesruled out.
世界各地的人们普遍感染人类冠状病毒229E、NL63、OC43和HKU1。Please seehttps://www.cdc.gov/coronavirus/types.htmlfor additional human coronaviruses that are captured through use of ICD-10 codes B34.2 and B97.2.
| CDC Value-set: COVID-19 Suspected | 世界杯葡萄牙vs加纳即时走地健康催化剂值集: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 casesruled 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.
| 世界杯葡萄牙vs加纳即时走地健康催化剂值集: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 thePatient-Grain Diagnosis Logicto help identify those patients who have developed severe symptoms/complications associated with C-19 likely requiring hospital-level care,in addition toother 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 thePatient-Grain Diagnosis Logicto help identify those patients who have symptoms of C-19in addition toother 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).19In 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.
本研究建立了实验室测试知识管理工作流程(图2)，为识别实验室测试类型和理解来自多个电子病历系统的实验室结果数据记录中表示的实验室结果值提供了知识库。这个实验室测试知识管理工作流程如下图所示。这个知识库来自多个电子病历系统的许多实验室结果记录，可以自动分类实验室结果(阳性、阴性、未决、模糊、测试问题、未映射)和实验室测试类型(检测SARS-CoV-2材料、检测SARS-CoV-2抗体)。该分类系统非常方便用于分析，以发现感兴趣的模式，例如，COVID-19确诊或疑似病例的患者数据。统一的实验室结果值集允许快速自动化地使用易于理解的数据(即阳性和阴性结果值)，识别可通过额外数据管理(即未映射)使用的结果，并能够选择仍在进行或无法进行检测(即等待检测)的患者。
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-19by 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].
- 世界卫生组织。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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