ICD Social Codes

Torres, Jacqueline M.; Lawlor, John; Colvin, Jeffrey D.; Sills, Marion R.; Bettenhausen, Jessica L.; Davidson, Amber; Cutler, Gretchen J.; Hall, Matt; Gottlieb, Laura M.

doi:10.1097/mlr.0000000000000764

Cited by 69 publications

(43 citation statements)

References 20 publications

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“…In another study, assessment of insurance claims and EHR data of older adults provided relatively similar results with only 0.03% of claims and 0.06% of EHR’s structured data providing information related to lack of social support [12,32]. Similarly, Torres et al [15] found SBDH codes being underutilized for tracking social needs using a national sample of hospital discharges (ie, <7% of discharges in any demographic or payer subgroup). Finally, Oreskovic et al [16] developed a systematic approach to identify psychosocial risk factors within any part of a patient’s EHR record and detected an average of approximately 14 SBDH-related codes/words per Medicaid enrollee.…”

Section: Discussionmentioning

confidence: 94%

Assessing the Availability of Data on Social and Behavioral Determinants in Structured and Unstructured Electronic Health Records: A Retrospective Analysis of a Multilevel Health Care System

Hatef

Rouhizadeh²,

Tia

et al. 2019

JMIR Med Inform

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Background Most US health care providers have adopted electronic health records (EHRs) that facilitate the uniform collection of clinical information. However, standardized data formats to capture social and behavioral determinants of health (SBDH) in structured EHR fields are still evolving and not adopted widely. Consequently, at the point of care, SBDH data are often documented within unstructured EHR fields that require time-consuming and subjective methods to retrieve. Meanwhile, collecting SBDH data using traditional surveys on a large sample of patients is infeasible for health care providers attempting to rapidly incorporate SBDH data in their population health management efforts. A potential approach to facilitate targeted SBDH data collection is applying information extraction methods to EHR data to prescreen the population for identification of immediate social needs. Objective Our aim was to examine the availability and characteristics of SBDH data captured in the EHR of a multilevel academic health care system that provides both inpatient and outpatient care to patients with varying SBDH across Maryland. Methods We measured the availability of selected patient-level SBDH in both structured and unstructured EHR data. We assessed various SBDH including demographics, preferred language, alcohol use, smoking status, social connection and/or isolation, housing issues, financial resource strains, and availability of a home address. EHR’s structured data were represented by information collected between January 2003 and June 2018 from 5,401,324 patients. EHR’s unstructured data represented information captured for 1,188,202 patients between July 2016 and May 2018 (a shorter time frame because of limited availability of consistent unstructured data). We used text-mining techniques to extract a subset of SBDH factors from EHR’s unstructured data. Results We identified a valid address or zip code for 5.2 million (95.00%) of approximately 5.4 million patients. Ethnicity was captured for 2.7 million (50.00%), whereas race was documented for 4.9 million (90.00%) and a preferred language for 2.7 million (49.00%) patients. Information regarding alcohol use and smoking status was coded for 490,348 (9.08%) and 1,728,749 (32.01%) patients, respectively. Using the International Classification of Diseases–10th Revision diagnoses codes, we identified 35,171 (0.65%) patients with information related to social connection/isolation, 10,433 (0.19%) patients with housing issues, and 3543 (0.07%) patients with income/financial resource strain. Of approximately 1.2 million unique patients with unstructured data, 30,893 (2.60%) had at least one clinical note containing phrases referring to social connection/isolation, 35,646 (3.00%) included housing issues, and 11,882 (1.00%) had mentions of financial resource strain. Conclusions Apart from demographics, SBDH data are not regularly collected for patients. Health care providers should assess the availability and characteristics of SBDH data in EHRs. Evaluating the quality of SBDH data can potentially enable health care providers to modify underlying workflows to improve the documentation, collection, and extraction of SBDH data from EHRs.

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Section: Discussionmentioning

confidence: 94%

Assessing the Availability of Data on Social and Behavioral Determinants in Structured and Unstructured Electronic Health Records: A Retrospective Analysis of a Multilevel Health Care System

Hatef

Rouhizadeh²,

Tia

et al. 2019

JMIR Med Inform

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“…SDH is a combination of lifestyle, behavioral, social, economic, and environmental factors that are powerful drivers of health and well-being [39]. Similar to geriatric syndromes, SDH are poorly captured by diagnostic codes in EHRs [40-42]. More broadly, our success with NLP techniques for geriatric syndromes suggests that other areas that actively use SDH information to bridge the gap between population and public health may similarly benefit from EHRs’ free text and NLP techniques [43-46].…”

Section: Discussionmentioning

confidence: 99%

Extraction of Geriatric Syndromes From Electronic Health Record Clinical Notes: Assessment of Statistical Natural Language Processing Methods

Chen¹,

Dredze²,

Weiner³

et al. 2019

JMIR Med Inform

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Background Geriatric syndromes in older adults are associated with adverse outcomes. However, despite being reported in clinical notes, these syndromes are often poorly captured by diagnostic codes in the structured fields of electronic health records (EHRs) or administrative records. Objective We aim to automatically determine if a patient has any geriatric syndromes by mining the free text of associated EHR clinical notes. We assessed which statistical natural language processing (NLP) techniques are most effective. Methods We applied conditional random fields (CRFs), a widely used machine learning algorithm, to identify each of 10 geriatric syndrome constructs in a clinical note. We assessed three sets of features and attributes for CRF operations: a base set, enhanced token, and contextual features. We trained the CRF on 3901 manually annotated notes from 85 patients, tuned the CRF on a validation set of 50 patients, and evaluated it on 50 held-out test patients. These notes were from a group of US Medicare patients over 65 years of age enrolled in a Medicare Advantage Health Maintenance Organization and cared for by a large group practice in Massachusetts. Results A final feature set was formed through comprehensive feature ablation experiments. The final CRF model performed well at patient-level determination (macroaverage F1=0.834, microaverage F1=0.851); however, performance varied by construct. For example, at phrase-partial evaluation, the CRF model worked well on constructs such as absence of fecal control (F1=0.857) and vision impairment (F1=0.798) but poorly on malnutrition (F1=0.155), weight loss (F1=0.394), and severe urinary control issues (F1=0.532). Errors were primarily due to previously unobserved words (ie, out-of-vocabulary) and a lack of context. Conclusions This study shows that statistical NLP can be used to identify geriatric syndromes from EHR-extracted clinical notes. This creates new opportunities to identify patients with geriatric syndromes and study their health outcomes.

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“…However, V‐codes are rarely used in EHRs, in less than 2 percent of hospitalizations in a national review of 2013 data 16 . There are many reasons for infrequent use of these codes.…”

Section: Introductionmentioning

confidence: 99%

Relative accuracy of social and medical determinants of suicide in electronic health records

Alemi

Avramovic

Renshaw

et al. 2020

Health Services Research

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Objective: This paper compares the accuracy of predicting suicide from Social Determinants of Health (SDoH) or history of illness. Population Studied: 5 313 965 Veterans who at least had two primary care visits between 2008 and 2016. Study Design: The dependent variable was suicide or intentional self-injury. The independent variables were 10 495 International Classification of Disease (ICD) Version 9 codes, age, and gender. The ICD codes included 40 V-codes used for measuring SDoH, such as family disruption, family history of substance abuse, lack of education, legal impediments, social isolation, unemployment, and homelessness. The sample was randomly divided into training (90 percent) and validation (10 percent) sets. Area under the receiver operating characteristic (AROC) was used to measure accuracy of predictions in the validation set. Principal Findings: Separate analyses were done for inpatient and outpatient codes; the results were similar. In the hospitalized group, the mean age was 67.2 years, and 92.1 percent were male. The mean number of medical diagnostic codes during the study period was 37; and 12.9 percent had at least one SDoH V-code. At least one episode of suicide or intentional self-injury occurred in 1.89 percent of cases. SDoH V-codes, on average, elevated the risk of suicide or intentional self-injury by 24-fold (ranging from 4to 86-fold). An index of 40 SDoH codes predicted suicide or intentional self-injury with an AROC of 0.64. An index of 10 445 medical diagnoses, without SDoH V-codes, had AROC of 0.77. The combined SDoH and medical diagnoses codes also had AROC of 0.77. Conclusion: In predicting suicide or intentional self-harm, SDoH V-codes add negligible information beyond what is already available in medical diagnosis codes. Implications for Practice: Policies that affect SDoH (eg, housing policies, resilience training) may not have an impact on suicide rates, if they do not change the underlying medical causes of SDoH. K E Y W O R D S high dimensional data, hierarchical modeling, mental health, naive Bayes, observational data/ quasi-experiments, self-harm, social determinants of health, stressful life events, suicide, VA health care system This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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ICD Social Codes

Abstract: SDH V codes are infrequently utilized in inpatient settings for discharges other than those related to mental health and alcohol/substance use. Utilization incentives will likely need to be developed to realize the potential benefits of cataloging SDH information.

Cited by 69 publications

References 20 publications

Assessing the Availability of Data on Social and Behavioral Determinants in Structured and Unstructured Electronic Health Records: A Retrospective Analysis of a Multilevel Health Care System

Assessing the Availability of Data on Social and Behavioral Determinants in Structured and Unstructured Electronic Health Records: A Retrospective Analysis of a Multilevel Health Care System

Extraction of Geriatric Syndromes From Electronic Health Record Clinical Notes: Assessment of Statistical Natural Language Processing Methods

Relative accuracy of social and medical determinants of suicide in electronic health records

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