Assessing the Effect of Electronic Health Record Data Quality on Identifying Patients with Type 2 Diabetes.

BACKGROUND: Increasing and substantial reliance on Electronic health records (EHR) and data types (i.e., diagnosis (Dx), medication (Rx), laboratory (Lx)) demands assessment of its data quality (DQ) as a fundamental approach; especially since there is need to identify appropriate denominator population with chronic conditions, such as Type-2 Diabetes (T2D), using commonly available computable phenotype definitions (phenotype). OBJECTIVE: To bridge this gap, our study aims to assess how issues of EHR DQ, and variations and robustness (or lack thereof) in phenotypes may have potential impact in identifying denominator population. METHODS: Approximately 208k patients with T2D were included in our study using retrospective EHR data of Johns Hopkins Medical Institution (JHMI) during 2017-2019. Our assessment included 4 published phenotypes, and 1 definition from a panel of experts at Hopkins. We conducted descriptive analyses of demographics (i.e., age, sex, race, ethnicity), healthcare utilization (inpatient and emergency room visits), and average Charlson Comorbidity score of each phenotype. We then used different methods to induce/simulate DQ issues of completeness, accuracy and timeliness separately across each phenotype. For induced data incompleteness, our model randomly dropped Dx, Rx, and Lx codes independently at increments of 10%; for induced data inaccuracy, our model randomly replaced a Dx or Rx code with another code of the same data type and induced 2% incremental change from -100% to +10% in Lx result values; and lastly, for timeliness, data was modeled for induced incremental shift of date records by 30 days up to a year. RESULTS: Less than a quarter (23%) of population overlapped across all phenotypes using EHR. The population identified by each phenotype varied across all combination of data types. Induced incompleteness identified fewer patients with each increment, for e.g., at 100% diagnostic incompleteness, Chronic Conditions Data Warehouse (CCW) phenotype identified zero patients as its phenotypic characteristics included only Dx codes. Induced inaccuracy and timeliness similarly demonstrated variations in performance of each phenotype and therefore, resulting in fewer patients being identified with each incremental change. CONCLUSIONS: We utilized EHR data with Dx, Rx, and Lx data types from a large tertiary hospital system to understand the T2D phenotypic differences and performance. We learned how issues of DQ, using induced DQ methods, may impact identification of the denominator populations upon which clinical (e.g., clinical research and trials, population health evaluations) and financial/operational decisions are made. The novel results from our study may inform in shaping a common T2D computable phenotype definition that can be applicable to clinical informatics, managing chronic conditions, and additional healthcare industry-wide efforts.

Assessing the Effect of Electronic Health Record Data Quality on Identifying Patients With Type 2 Diabetes: Cross-Sectional Study.

Background: Increasing and substantial reliance on electronic health records (EHRs) and data types (ie, diagnosis, medication, and laboratory data) demands assessment of their data quality as a fundamental approach, especially since there is a need to identify appropriate denominator populations with chronic conditions, such as type 2 diabetes (T2D), using commonly available computable phenotype definitions (ie, phenotypes). Objective: To bridge this gap, our study aims to assess how issues of EHR data quality and variations and robustness (or lack thereof) in phenotypes may have potential impacts in identifying denominator populations. Methods: Approximately 208,000 patients with T2D were included in our study, which used retrospective EHR data from the Johns Hopkins Medical Institution (JHMI) during 2017-2019. Our assessment included 4 published phenotypes and 1 definition from a panel of experts at Hopkins. We conducted descriptive analyses of demographics (ie, age, sex, race, and ethnicity), use of health care (inpatient and emergency room visits), and the average Charlson Comorbidity Index score of each phenotype. We then used different methods to induce or simulate data quality issues of completeness, accuracy, and timeliness separately across each phenotype. For induced data incompleteness, our model randomly dropped diagnosis, medication, and laboratory codes independently at increments of 10%; for induced data inaccuracy, our model randomly replaced a diagnosis or medication code with another code of the same data type and induced 2% incremental change from -100% to +10% in laboratory result values; and lastly, for timeliness, data were modeled for induced incremental shift of date records by 30 days to 365 days. Results: Less than a quarter (n=47,326, 23%) of the population overlapped across all phenotypes using EHRs. The population identified by each phenotype varied across all combinations of data types. Induced incompleteness identified fewer patients with each increment; for example, at 100% diagnostic incompleteness, the Chronic Conditions Data Warehouse phenotype identified zero patients, as its phenotypic characteristics included only diagnosis codes. Induced inaccuracy and timeliness similarly demonstrated variations in performance of each phenotype, therefore resulting in fewer patients being identified with each incremental change. Conclusions: We used EHR data with diagnosis, medication, and laboratory data types from a large tertiary hospital system to understand T2D phenotypic differences and performance. We used induced data quality methods to learn how data quality issues may impact identification of the denominator populations upon which clinical (eg, clinical research and trials, population health evaluations) and financial or operational decisions are made. The novel results from our study may inform future approaches to shaping a common T2D computable phenotype definition that can be applied to clinical informatics, managing chronic conditions, and additional industry-wide efforts in health care.

Correction: Assessing the Racial and Socioeconomic Disparities in Postpartum Depression Using Population-Level Hospital Discharge Data: Longitudinal Retrospective Study.

[This corrects the article DOI: 10.2196/38879.].

A framework for understanding an open scientific community using automated harvesting of public artifacts.

Background: The Observational Health Data Sciences and Informatics (OHDSI) community has emerged as a leader in observational research on real-world clinical data for promoting evidence for healthcare and decision-making. The community has seen rapid growth in publications, citations, and the number of authors. Components of its successful uptake have been attributed to an open science and collaborative culture for research and development. Investigating the adoption of OHDSI as a field of study provides an opportunity to understand how communities embrace new ideas, onboard new members, and enhance their impact. Objective: To track, study, and evaluate an open scientific community's growth and impact. Method: We present a modern architecture leveraging open application programming interfaces to capture publicly available data (PubMed, YouTube, and EHDEN) on open science activities (publication, teaching, and engagement). Results: Three interactive dashboard were implemented for each publicly available artifact (PubMed, YouTube, and EHDEN). Each dashboard provides longitudinal summary analysis and has a searchable table, which differs in the available features related to each public artifact. Conclusion: We discuss the insights enabled by our approach to monitor the growth and impact of the OHDSI community by capturing artifacts of learning, teaching, and creation. We share the implications for different users based on their functional needs. As other scientific networks adopt open-source frameworks, our framework serves as a model for tracking the growth of their community, driving the perception of their development, engaging their members, and attaining higher impact.

Association of collaborative care intervention features with depression and metabolic outcomes in the INDEPENDENT study: A mixed methods study.

AIMS: The INtegrating DEPrEssioN and Diabetes treatmENT (INDEPENDENT) trial tested a collaborative care model including electronic clinical decision support (CDS) for treating diabetes and depression in India. We aimed to assess which features of this clinically and cost-effective intervention were associated with improvements in diabetes and depression measures. METHODS: Post-hoc analysis of the INDEPENDENT trial data (189 intervention participants) was conducted to determine each intervention feature's effect: 1. Collaborative case reviews between expert psychiatrists and the care team; 2. Patient care-coordinator contacts; and 3. Clinicians' CDS prompt modifications. Primary outcome was baseline-to-12-months improvements in diabetes control, blood pressure, cholesterol, and depression. Implementer interviews revealed barriers and facilitators of intervention success. Joint displays integrated mixed methods' results. RESULTS: High baseline HbA1c≥ 74.9 mmol/mol (9%) was associated with 5.72 fewer care-coordinator contacts than those with better baseline HbA1c (76.8 mmol/mol, 9.18%, p < 0.001). Prompt modification proportions varied from 38.3% (diabetes) to 1.3% (LDL). Interviews found that providers' and participants' visit frequencies were preference dependent. Qualitative data elucidated patient-level factors that influenced number of clinical contacts and prompt modifications explaining their lack of association with clinical outcomes. CONCLUSION: Our mixed methods approach underlines the importance of the complementarity of different intervention features. Qualitative findings further illuminate reasons for variations in fidelity from the core model.

Applicability Area: A novel utility-based approach for evaluating predictive models, beyond discrimination.

Translating prediction models into practice and supporting clinicians' decision-making demand demonstration of clinical value. Existing approaches to evaluating machine learning models emphasize discriminatory power, which is only a part of the medical decision problem. We propose the Applicability Area (ApAr), a decision-analytic utility-based approach to evaluating predictive models that communicate the range of prior probability and test cutoffs for which the model has positive utility; larger ApArs suggest a broader potential use of the model. We assess ApAr with simulated datasets and with three published medical datasets. ApAr adds value beyond the typical area under the receiver operating characteristic curve (AUROC) metric analysis. As an example, in the diabetes dataset, the top model by ApAr was ranked as the 23rd best model by AUROC. Decision makers looking to adopt and implement models can leverage ApArs to assess if the local range of priors and utilities is within the respective ApArs.

Assessing the Racial and Socioeconomic Disparities in Postpartum Depression Using Population-Level Hospital Discharge Data: Longitudinal Retrospective Study.

BACKGROUND: In the United States, >3.6 million deliveries occur annually. Among them, up to 20% (approximately 700,000) of women experience postpartum depression (PPD) according to the Centers for Disease Control and Prevention. Absence of accurate reporting and diagnosis has made phenotyping of patients with PPD difficult. Existing literature has shown that factors such as race, socioeconomic status, and history of substance abuse are associated with the differential risks of PPD. However, limited research has considered differential temporal associations with the outcome. OBJECTIVE: This study aimed to estimate the disparities in the risk of PPD and time to diagnosis for patients of different racial and socioeconomic backgrounds. METHODS: This is a longitudinal retrospective study using the statewide hospital discharge data from Maryland. We identified 160,066 individuals who had a hospital delivery from 2017 to 2019. We applied logistic regression and Cox regression to study the risk of PPD across racial and socioeconomic strata. Multinomial regression was used to estimate the risk of PPD at different postpartum stages. RESULTS: The cumulative incidence of PPD diagnosis was highest for White patients (8779/65,028, 13.5%) and lowest for Asian and Pacific Islander patients (248/10,760, 2.3%). Compared with White patients, PPD diagnosis was less likely to occur for Black patients (odds ratio [OR] 0.31, 95% CI 0.30-0.33), Asian or Pacific Islander patients (OR 0.17, 95% CI 0.15-0.19), and Hispanic patients (OR 0.21, 95% CI 0.19-0.22). Similar findings were observed from the Cox regression analysis. Multinomial regression showed that compared with White patients, Black patients (relative risk 2.12, 95% CI 1.73-2.60) and Asian and Pacific Islander patients (relative risk 2.48, 95% CI 1.46-4.21) were more likely to be diagnosed with PPD after 8 weeks of delivery. CONCLUSIONS: Compared with White patients, PPD diagnosis is less likely to occur in individuals of other races. We found disparate timing in PPD diagnosis across different racial groups and socioeconomic backgrounds. Our findings serve to enhance intervention strategies and policies for phenotyping patients at the highest risk of PPD and to highlight needs in data quality to support future work on racial disparities in PPD.

Impact of social determinants of health on improving the LACE index for 30-day unplanned readmission prediction.

Objective: Early and accurate prediction of patients at risk of readmission is key to reducing costs and improving outcomes. LACE is a widely used score to predict 30-day readmissions. We examine whether adding social determinants of health (SDOH) to LACE can improve its predictive performance. Methods: This is a retrospective study that included all inpatient encounters in the state of Maryland in 2019. We constructed predictive models by fitting Logistic Regression (LR) on LACE and different sets of SDOH predictors. We used the area under the curve (AUC) to evaluate discrimination and SHapley Additive exPlanations values to assess feature importance. Results: Our study population included 316 558 patients of whom 35 431 (11.19%) patients were readmitted after 30 days. Readmitted patients had more challenges with individual-level SDOH and were more likely to reside in communities with poor SDOH conditions. Adding a combination of individual and community-level SDOH improved LACE performance from AUC = 0.698 (95% CI [0.695-0.7]; ref) to AUC = 0.708 (95% CI [0.705-0.71]; P < .001). The increase in AUC was highest in black patients (+1.6), patients aged 65 years or older (+1.4), and male patients (+1.4). Discussion: We demonstrated the value of SDOH in improving the LACE index. Further, the additional predictive value of SDOH on readmission risk varies by subpopulations. Vulnerable populations like black patients and the elderly are likely to benefit more from the inclusion of SDOH in readmission prediction. Conclusion: These findings provide potential SDOH factors that health systems and policymakers can target to reduce overall readmissions.