Revised Recommendations on Methods for Assessing Multimorbidity Changes over Time: Aligning the Method to the Purpose.

BACKGROUND: The rapidly growing field of multimorbidity research demonstrates that changes in multimorbidity in mid- and late-life have far reaching effects on important person-centered outcomes, such as health-related quality of life. However, there are few organizing frameworks and comparatively little work weighing the merits and limitations of various quantitative methods applied to the longitudinal study of multimorbidity. METHODS: We identify and discuss methods aligned to specific research objectives with the goals of 1) establishing a common language for assessing longitudinal changes in multimorbidity, 2) illuminating gaps in our knowledge regarding multimorbidity progression and critical periods of change, and 3) informing research to identify groups that experience different rates and divergent etiological pathways of disease progression linked to deterioration in important health-related outcomes. RESULTS: We review practical issues in the measurement of multimorbidity, longitudinal analysis of health-related data, operationalizing change over time, and discuss methods that align with four general typologies for research objectives in the longitudinal study of multimorbidity: 1) examine individual change in multimorbidity, 2) identify sub-groups that follow similar trajectories of multimorbidity progression, 3) understand when, how, and why individuals or groups shift to more advanced stages of multimorbidity, and 4) examine the co-progression of multimorbidity with key health domains. CONCLUSION: This work encourages a systematic approach to the quantitative study of change in multimorbidity and provides a valuable resource for researchers working to measure and minimize the deleterious effects of multimorbidity on aging populations.

Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study.

Background: SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials. Methods: Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis. Findings: Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]). Interpretation: In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD. Funding: National Institutes of Health, United States Department of Veterans Affairs.

Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM.

Objective: To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin. Design: Federated pharmacoepidemiological evaluation in LEGEND-T2DM. Setting: 10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021. Participants: 4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments. Exposure: The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort. Main outcomes measures: The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated. Results: 4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease. Conclusions: Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.

Maturity in enterprise data warehouses for research operations: Analysis of a pilot study.

Enterprise data warehouses for research (EDW4R) is a critical component of National Institutes of Health Clinical and Translational Science Award (CTSA) hubs. EDW4R operations have unique needs that require specialized skills and collaborations across multiple domains which limit the ability to apply existing models of information technology (IT) performance. Because of this uniqueness, we developed a new EDW4R maturity model based on prior qualitative study of operational practices for supporting EDW4Rs at CTSA hubs. In a pilot study, respondents from fifteen CTSA hubs completed the novel EDW4R maturity index survey by rating 33 maturity statements across 6 categories using a 5-point Likert scale. Of the six categories, respondents rated workforce as most mature (4.17 [3.67-4.42]) and relationship with enterprise IT as the least mature (3.00 [2.80-3.80]). Our pilot of a novel maturity index shows a baseline quantitative measure of EDW4R functions across fifteen CTSA hubs. The maturity index may be useful to faculty and staff currently leading an EDW4R by creating opportunities to explore the index in local context and comparison to other institutions.

Development and Evaluation of Serious Illness Conversation Training for Interprofessional Primary Care Teams.

Background: Early advance care planning (ACP) conversations are essential to deliver patient-centered care. While primary care is an ideal setting to initiate ACP, such as Serious Illness Conversations (SICs), many barriers exist to implement such conversations in routine practice. An interprofessional team approach holds promises to address barriers. Objective: To develop and evaluate SIC training for interprofessional primary care teams (IP-SIC). Design: An existing SIC training was adapted for IP-SIC and then implemented and evaluated for acceptability and effectiveness. Setting/Context: Interprofessional teams in 15 primary care clinics in five US states. Measures: Acceptability of the IP-SIC training and participants' self-reported likelihood to engage in ACP after the training. Results: The 156 participants were a mix of physicians and advanced practice providers (APPs) (44%), nurses and social workers (31%), and others (25%). More than 90% of all participants rated the IP-SIC training positively. While nurse/social worker and other groups were less likely than physician and APP group to engage in ACP before training (4.4, 3.7, and 6.4 on a 1-10 scale, respectively), all groups showed significant increase in likelihood to engage in ACP after the IP-SIC training (8.5, 7.7, and 9.2, respectively). Both physician/APP and nurse/social worker groups showed significant increase in likelihood to use the SIC Guide after the IP-SIC training, whereas an increase in likelihood to use SIC Guide among other groups was not statistically significant. Conclusion: The new IP-SIC training was well accepted by interprofessional team members and effective to improve their likelihood to engage in ACP. Further research exploring how to facilitate collaboration among interprofessional team members to maximize opportunities for more and better ACP is warranted. ClinicalTrials.gov ID: NCT03577002.

Harnessing the Promise of Artificial Intelligence Responsibly.

This Viewpoint discusses the benefits and potential harms of using artificial intelligence (AI) algorithms in medicine and proposes the collaborative creation of a Code of Conduct for AI in Health Care.

Racial, ethnic, and socioeconomic disparities in trajectories of morbidity accumulation among older Americans.

Introduction: Multimorbidity, the presence of multiple chronic health conditions, generally starts in middle and older age but there is considerable heterogeneity in the trajectory of morbidity accumulation. This study aimed to clarify the number of distinct trajectories and the potential associations between race/ethnicity and socioeconomic status and these trajectories. Methods: Data from 13,699 respondents (age ≥51) in the Health and Retirement Study between 1998 and 2016 were analyzed with growth mixture models. Nine prevalent self-reported morbidities (arthritis, cancer, cognitive impairment, depressive symptoms, diabetes, heart disease, hypertension, lung disease, stroke) were summed for the morbidity count. Results: Three trajectories of morbidity accumulation were identified: low [starting with few morbidities and accumulating them slowly (i.e., low intercept and low slope); 80% of sample], increasing (i.e., low intercept and high slope; 9%), and high (i.e., high intercept and low slope; 11%). Compared to non-Hispanic (NH) White adults in covariate-adjusted models, NH Black adults had disadvantages while Hispanic adults had advantages. Our results suggest a protective effect of education for NH Black adults (i.e., racial health disparities observed at low education were ameliorated and then eliminated at increasing levels of education) and a reverse pattern for Hispanic adults (i.e., increasing levels of education was found to dampen the advantages Hispanic adults had at low education). Compared with NH White adults, higher levels of wealth were protective for both NH Black adults (i.e., reducing or reversing racial health disparities observed at low wealth) and Hispanic adults (i.e., increasing the initial health advantages observed at low wealth). Conclusion: These findings have implications for addressing health disparities through more precise targeting of public health interventions. This work highlights the imperative to address socioeconomic inequalities that interact with race/ethnicity in complex ways to erode health.

Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse.

OBJECTIVES: Collider bias is a common threat to internal validity in clinical research but is rarely mentioned in informatics education or literature. Conditioning on a collider, which is a variable that is the shared causal descendant of an exposure and outcome, may result in spurious associations between the exposure and outcome. Our objective is to introduce readers to collider bias and its corollaries in the retrospective analysis of electronic health record (EHR) data. TARGET AUDIENCE: Collider bias is likely to arise in the reuse of EHR data, due to data-generating mechanisms and the nature of healthcare access and utilization in the United States. Therefore, this tutorial is aimed at informaticians and other EHR data consumers without a background in epidemiological methods or causal inference. SCOPE: We focus specifically on problems that may arise from conditioning on forms of healthcare utilization, a common collider that is an implicit selection criterion when one reuses EHR data. Directed acyclic graphs (DAGs) are introduced as a tool for identifying potential sources of bias during study design and planning. References for additional resources on causal inference and DAG construction are provided.

A multi-site randomized trial of a clinical decision support intervention to improve problem list completeness.

OBJECTIVE: To improve problem list documentation and care quality. MATERIALS AND METHODS: We developed algorithms to infer clinical problems a patient has that are not recorded on the coded problem list using structured data in the electronic health record (EHR) for 12 clinically significant heart, lung, and blood diseases. We also developed a clinical decision support (CDS) intervention which suggests adding missing problems to the problem list. We evaluated the intervention at 4 diverse healthcare systems using 3 different EHRs in a randomized trial using 3 predetermined outcome measures: alert acceptance, problem addition, and National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (NCQA HEDIS) clinical quality measures. RESULTS: There were 288 832 opportunities to add a problem in the intervention arm and the problem was added 63 777 times (acceptance rate 22.1%). The intervention arm had 4.6 times as many problems added as the control arm. There were no significant differences in any of the clinical quality measures. DISCUSSION: The CDS intervention was highly effective at improving problem list completeness. However, the improvement in problem list utilization was not associated with improvement in the quality measures. The lack of effect on quality measures suggests that problem list documentation is not directly associated with improvements in quality measured by National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (NCQA HEDIS) quality measures. However, improved problem list accuracy has other benefits, including clinical care, patient comprehension of health conditions, accurate CDS and population health, and for research. CONCLUSION: An EHR-embedded CDS intervention was effective at improving problem list completeness but was not associated with improvement in quality measures.

Depressive Multimorbidity and Trajectories of Functional Status among Older Americans: Differences by Racial/Ethnic Group.

OBJECTIVE: This study aims to evaluate the impact of depressive multimorbidity (ie, including depressive symptoms) on the long-term development of activities of daily living (ADL) and instrumental activities of daily living (IADL) limitations according to racial/ethnic group in a representative sample of US older adults. DESIGN: Prospective, observational, population-based 16-year follow-up study of nationally representative sample. SETTING AND PARTICIPANTS: Sample of older non-Hispanic Black, Hispanic, and nonHispanic White Americans from the Health and Retirement Study (2000‒2016, N = 16,364, community-dwelling adults ≥65 years of age). METHODS: Data from 9 biennial assessments were used to evaluate the accumulation of ADL-IADL limitations (range 0‒11) among participants with depressive (8-item Center for Epidemiologic Studies Depression score≥4) vs somatic (ie, physical conditions only) multimorbidity vs those without multimorbidity (no or 1 condition). Generalized estimating equations included race/ethnicity (non-Hispanic Black, Hispanic, non-Hispanic White), baseline age, sex, body mass index, education, partnered, and net worth. RESULTS: Depressive and somatic multimorbidity were associated with 5.18 and 2.95 times greater accumulation of functional limitations, respectively, relative to no disease [incidence rate ratio (IRR) = 5.18, 95% confidence interval, CI (4.38,6.13), IRR = 2.95, 95% CI (2.51,3.48)]. Hispanic and Black respondents experienced greater accumulation of ADL-IADL limitations than White respondents [IRR = 1.27, 95% CI (1.14, 1.41), IRR = 1.31, 95% CI (1.20, 1.43), respectively]. CONCLUSIONS AND IMPLICATIONS: Combinations of somatic diseases and high depressive symptoms are associated with greatest accumulation of functional limitations over time in adults ages 65 and older. There is a more rapid growth in functional limitations among individuals from racial/ethnic minority groups. Given the high prevalence of multimorbidity and depressive symptomatology among older adults and the availability of treatment options for depression, these results highlight the importance of screening/treatment for depression, particularly among older adults with socioeconomic vulnerabilities, to slow the progression of functional decline in later life.

How Are Leading Research Institutions Engaging with Data Sharing Tools and Programs?

With widespread electronic health record (EHR) adoption and improvements in health information interoperability in the United States, troves of data are available for knowledge discovery. Several data sharing programs and tools have been developed to support research activities, including efforts funded by the National Institutes of Health (NIH), EHR vendors, and other public- and private-sector entities. We surveyed 65 leading research institutions (77% response rate) about their use of and value derived from ten programs/tools, including NIH's Accrual to Clinical Trials, Epic Corporation's Cosmos, and the Observational Health Data Sciences and Informatics consortium. Most institutions participated in multiple programs/tools but reported relatively low usage (even when they participated, they frequently indicated that fewer than one individual/month benefitted from the platform to support research activities). Our findings suggest that investments in research data sharing have not yet achieved desired results.

Multidimensional trajectories of multimorbidity, functional status, cognitive performance, and depressive symptoms among diverse groups of older adults.

Background: Inter-relationships between multimorbidity and geriatric syndromes are poorly understood. This study assesses heterogeneity in joint trajectories of somatic disease, functional status, cognitive performance, and depressive symptomatology. Methods: We analyzed 16 years of longitudinal data from the Health and Retirement Study (HRS, 1998-2016) for n = 11,565 older adults (≥65 years) in the United States. Group-based mixture modeling identified latent clusters of older adults following similar joint trajectories across domains. Results: We identified four distinct multidimensional trajectory groups: (1) Minimal Impairment with Low Multimorbidity (32.7% of the sample; mean = 0.60 conditions at age 65, 2.1 conditions at age 90) had limited deterioration; (2) Minimal Impairment with High Multimorbidity (32.9%; mean = 2.3 conditions at age 65, 4.0 at age 90) had minimal deterioration; (3) Multidomain Impairment with Intermediate Multimorbidity (19.9%; mean = 1.3 conditions at age 65, 2.7 at age 90) had moderate depressive symptomatology and functional impariments with worsening cognitive performance; (4) Multidomain Impairment with High Multimorbidity (14.1%; mean = 3.3 conditions at age 65; 4.7 at age 90) had substantial functional limitation and high depressive symptomatology with worsening cognitive performance. Black and Hispanic race/ethnicity, lower wealth, lower education, male sex, and smoking history were significantly associated with membership in the two Multidomain Impairment classes. Conclusions: There is substantial heterogeneity in combined trajectories of interrelated health domains in late life. Membership in the two most impaired classes was more likely for minoritized older adults.

Provider Perspectives on Patient- and Provider-Facing High Blood Pressure Clinical Decision Support.

BACKGROUND: Hypertension, persistent high blood pressures (HBP) leading to chronic physiologic changes, is a common condition that is a major predictor of heart attacks, strokes, and other conditions. Despite strong evidence, care teams and patients are inconsistently adherent to HBP guideline recommendations. Patient-facing clinical decision support (CDS) could help improve recommendation adherence but must also be acceptable to clinicians and patients. OBJECTIVE: This study aimed to partly address the challenge of developing a patient-facing CDS application, we sought to understand provider variations and rationales related to HBP guideline recommendations and perceptions regarding patient role and use of digital tools. METHODS: We engaged hypertension experts and primary care respondents to iteratively develop and implement a pilot survey and a final survey which presented five clinical cases that queried clinicians' attitudes related to actions; variations; prioritization; patient input; importance; and barriers for HBP diagnosis, monitoring, and treatment. Analysis of Likert's scale scores was descriptive with content analysis for free-text answers. RESULTS: Fifteen hypertension experts and 14 providers took the pilot and final version of the surveys, respectively. The majority (>80%) of providers felt the recommendations were important, yet found them difficult to follow-up to 90% of the time. Perceptions of relative amounts of patient input and patient work for effective HBP management ranged from 22 to 100%. Stated reasons for variation included adverse effects of treatment, patient comorbidities, shared decision-making, and health care cost and access issues. Providers were generally positive toward patient use of electronic CDS applications but worried about access to health care, nuance of recommendations, and patient understanding of the tools. CONCLUSION: At baseline, provider management of HBP is heterogeneous. Providers were accepting of patient-facing CDS but reported preferences for that CDS to capture the complexity and nuance of guideline recommendations.

Issues With Variability in Electronic Health Record Data About Race and Ethnicity: Descriptive Analysis of the National COVID Cohort Collaborative Data Enclave.

BACKGROUND: The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity are collected and structured by health care organizations. OBJECTIVE: This study aims to evaluate and describe variations in how health care systems collect and report information about the race and ethnicity of their patients and to assess how well these data are integrated when aggregated into a large clinical database. METHODS: At the time of our analysis, the National COVID Cohort Collaborative (N3C) Data Enclave contained records from 6.5 million patients contributed by 56 health care institutions. We quantified the variability in the harmonized race and ethnicity data in the N3C Data Enclave by analyzing the conformance to health care standards for such data. We conducted a descriptive analysis by comparing the harmonized data available for research purposes in the database to the original source data contributed by health care institutions. To make the comparison, we tabulated the original source codes, enumerating how many patients had been reported with each encoded value and how many distinct ways each category was reported. The nonconforming data were also cross tabulated by 3 factors: patient ethnicity, the number of data partners using each code, and which data models utilized those particular encodings. For the nonconforming data, we used an inductive approach to sort the source encodings into categories. For example, values such as "Declined" were grouped with "Refused," and "Multiple Race" was grouped with "Two or more races" and "Multiracial." RESULTS: "No matching concept" was the second largest harmonized concept used by the N3C to describe the race of patients in their database. In addition, 20.7% of the race data did not conform to the standard; the largest category was data that were missing. Hispanic or Latino patients were overrepresented in the nonconforming racial data, and data from American Indian or Alaska Native patients were obscured. Although only a small proportion of the source data had not been mapped to the correct concepts (0.6%), Black or African American and Hispanic/Latino patients were overrepresented in this category. CONCLUSIONS: Differences in how race and ethnicity data are conceptualized and encoded by health care institutions can affect the quality of the data in aggregated clinical databases. The impact of data quality issues in the N3C Data Enclave was not equal across all races and ethnicities, which has the potential to introduce bias in analyses and conclusions drawn from these data. Transparency about how data have been transformed can help users make accurate analyses and inferences and eventually better guide clinical care and public policy.

Advancing Interoperability of Patient-level Social Determinants of Health Data to Support COVID-19 Research.

Including social determinants of health (SDoH) data in health outcomes research is essential for studying the sources of healthcare disparities and developing strategies to mitigate stressors. In this report, we describe a pragmatic design and approach to explore the encoding needs for transmitting SDoH screening tool responses from a large safety-net hospital into the National Covid Cohort Collaborative (N3C) OMOP dataset. We provide a stepwise account of designing data mapping and ingestion for patient-level SDoH and summarize the results of screening. Our approach demonstrates that sharing of these important data - typically stored as non-standard, EHR vendor specific codes - is feasible. As SDoH screening gains broader use nationally, the approach described in this paper could be used for other screening instruments and improve the interoperability of these important data.

Prediction of Future Health Care Utilization.

Psychosocial factors are known to have adverse health impacts, but are rarely measured; using natural language processing, we extracted factors that identified a higher risk segment of older adults with multimorbidity. We find these extracted features are highly predictive of future emergency department visits and hospitalizations, although only marginal prediction gains are seen compared to other models without these factors. Combining these extraction techniques with other measures of social determinants may help catalyze population health efforts to mitigate these health impacts.

Prediction of Future Health Care Utilization Through Note-extracted Psychosocial Factors.

BACKGROUND: Persons with multimorbidity (≥2 chronic conditions) face an increased risk of poor health outcomes, especially as they age. Psychosocial factors such as social isolation, chronic stress, housing insecurity, and financial insecurity have been shown to exacerbate these outcomes, but are not routinely assessed during the clinical encounter. Our objective was to extract these concepts from chart notes using natural language processing and predict their impact on health care utilization for patients with multimorbidity. METHODS: A cohort study to predict the 1-year likelihood of hospitalizations and emergency department visits for patients 65+ with multimorbidity with and without psychosocial factors. Psychosocial factors were extracted from narrative notes; all other covariates were extracted from electronic health record data from a large academic medical center using validated algorithms and concept sets. Logistic regression was performed to predict the likelihood of hospitalization and emergency department visit in the next year. RESULTS: In all, 76,479 patients were eligible; the majority were White (89%), 54% were female, with mean age 73. Those with psychosocial factors were older, had higher baseline utilization, and more chronic illnesses. The 4 psychosocial factors all independently predicted future utilization (odds ratio=1.27-2.77, C -statistic=0.63). Accounting for demographics, specific conditions, and previous utilization, 3 of 4 of the extracted factors remained predictive (odds ratio=1.13-1.86) for future utilization. Compared with models with no psychosocial factors, they had improved discrimination. Individual predictions were mixed, with social isolation predicting depression and morbidity; stress predicting atherosclerotic cardiovascular disease onset; and housing insecurity predicting substance use disorder morbidity. DISCUSSION: Psychosocial factors are known to have adverse health impacts, but are rarely measured; using natural language processing, we extracted factors that identified a higher risk segment of older adults with multimorbidity. Combining these extraction techniques with other measures of social determinants may help catalyze population health efforts to address psychosocial factors to mitigate their health impacts.

Generating and Reporting Electronic Clinical Quality Measures from Electronic Health Records: Strategies from EvidenceNOW Cooperatives.

BACKGROUND: Electronic clinical quality measures (eCQMs) from electronic health records (EHRs) are a key component of quality improvement (QI) initiatives in small-to-medium size primary care practices, but using eCQMs for QI can be challenging. Organizational strategies are needed to effectively operationalize eCQMs for QI in these practice settings. OBJECTIVE: This study aimed to characterize strategies that seven regional cooperatives participating in the EvidenceNOW initiative developed to generate and report EHR-based eCQMs for QI in small-to-medium size practices. METHODS: A qualitative study comprised of 17 interviews with representatives from all seven EvidenceNOW cooperatives was conducted. Interviewees included administrators were with both strategic and cooperative-level operational responsibilities and external practice facilitators were with hands-on experience helping practices use EHRs and eCQMs. A subteam conducted 1-hour semistructured telephone interviews with administrators and practice facilitators, then analyzed interview transcripts using immersion crystallization. The analysis and a conceptual model were vetted and approved by the larger group of coauthors. RESULTS: Cooperative strategies consisted of efforts in four key domains. First, cooperative adaptation shaped overall strategies for calculating eCQMs whether using EHRs, a centralized source, or a "hybrid strategy" of the two. Second, the eCQM generation described how EHR data were extracted, validated, and reported for calculating eCQMs. Third, practice facilitation characterized how facilitators with backgrounds in health information technology (IT) delivered services and solutions for data capture and quality and practice support. Fourth, performance reporting strategies and tools informed QI efforts and how cooperatives could alter their approaches to eCQMs. CONCLUSION: Cooperatives ultimately generated and reported eCQMs using hybrid strategies because they determined neither EHRs alone nor centralized sources alone could operationalize eCQMs for QI. This required cooperatives to devise solutions and utilize resources that often are unavailable to typical small-to-medium-sized practices. The experiences from EvidenceNOW cooperatives provide insights into how organizations can plan for challenges and operationalize EHR-based eCQMs.

Understanding enterprise data warehouses to support clinical and translational research: enterprise information technology relationships, data governance, workforce, and cloud computing.

OBJECTIVE: Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, effective approaches for enterprise data warehouses for research (EDW4R) development, maintenance, and sustainability remain unclear. The goal of this qualitative study was to understand CTSA EDW4R operations within the broader contexts of academic medical centers and technology. MATERIALS AND METHODS: We performed a directed content analysis of transcripts generated from semistructured interviews with informatics leaders from 20 CTSA hubs. RESULTS: Respondents referred to services provided by health system, university, and medical school information technology (IT) organizations as "enterprise information technology (IT)." Seventy-five percent of respondents stated that the team providing EDW4R service at their hub was separate from enterprise IT; strong relationships between EDW4R teams and enterprise IT were critical for success. Managing challenges of EDW4R staffing was made easier by executive leadership support. Data governance appeared to be a work in progress, as most hubs reported complex and incomplete processes, especially for commercial data sharing. Although nearly all hubs (n = 16) described use of cloud computing for specific projects, only 2 hubs reported using a cloud-based EDW4R. Respondents described EDW4R cloud migration facilitators, barriers, and opportunities. DISCUSSION: Descriptions of approaches to how EDW4R teams at CTSA hubs work with enterprise IT organizations, manage workforces, make decisions about data, and approach cloud computing provide insights for institutions seeking to leverage patient data for research. CONCLUSION: Identification of EDW4R best practices is challenging, and this study helps identify a breadth of viable options for CTSA hubs to consider when implementing EDW4R services.