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Objective: To review evidence regarding the use of Health Information Technology (health IT) interventions aimed at improving care for people living with multiple chronic conditions (PLWMCC) in order to identify critical knowledge gaps. Data Sources: We searched MEDLINE, CINAHL, PsycINFO, EMBASE, Compendex, and IEEE Xplore databases for studies published in English between 2010 and 2020. Study Design: We identified studies of health IT interventions for PLWMCC across three domains as follows: self‐management support, care coordination, and algorithms to support clinical decision making. Data Collection/Extraction Methods: Structured search queries were created and validated. Abstracts were reviewed iteratively to refine inclusion and exclusion criteria. The search was supplemented by manually searching the bibliographic sections of the included studies. The search included a forward citation search of studies nested within a clinical trial to identify the clinical trial protocol and published clinical trial results. Data were extracted independently by two reviewers. Principal Findings: The search yielded 1907 articles; 44 were included. Nine randomized controlled trials (RCTs) and 35 other studies including quasi‐experimental, usability, feasibility, qualitative studies, or development/validation studies of analytic models were included. Five RCTs had positive results, and the remaining four RCTs showed that the interventions had no effect. The studies address individual patient engagement and assess patient‐centered outcomes such as quality of life. Few RCTs assess outcomes such as disability and none assess mortality. Conclusions: Despite a growing body of literature on health IT interventions or multicomponent interventions including a health IT component for chronic disease management, current evidence for applying health IT solutions to improve care for PLWMCC is limited. The body of literature included in this review provides critical information on the state of the science as well as the many gaps that need to be filled for digital health to fulfill its promise in supporting care delivery that meets the needs of PLWMCC. [ABSTRACT FROM AUTHOR]

Outcomes: 1. Summarize their understanding of how advance care planning can be implemented and studied in primary care using team-based and clinician-focused models 2. Articulate how they interpret results showing similar outcomes across the model and how this may have been impacted by the COVID-19 pandemic Advance care planning (ACP) has the potential to ensure that healthcare supports what is important to patients and families. Seven Practice-Based Research Networks (PBRNs) in the US and Canada conducted a cluster randomized trial comparing team-based to clinician-focused ACP. 42 practices were randomized. A 3-hour training and practice facilitation supported practice implementation. Patients completed questionnaires after initial ACP conversations and 6 months later. Primary outcomes were the patient-reported goal concordant care and days at home. 38 practices completed the study; 50% rural and 80% were family medicine. 448 staff completed training. 1,131 patients and family care partners consented. Data from 802 patients were included in the intention-to-treat analyses of 6-month outcomes. Patients reporting a high goal-concordant care were slightly, but not statistically significantly, lower in the team (57%) vs. the clinician arm (63%) [unadjusted OR 0.79(CI 0.53 to 1.18); adjusted OR 0.80, (CI 0.53-1.20)]. Days at home were high: 83% in the team-based arm vs. 85% in the clinician arm had less than 2 days spent in hospital, ED, or nursing home [Unadjusted OR 0.84 (CI 0.48-1.45); adjusted OR 0.90(CI 0.50 -1.64)]. In the clinician arm, high goal concordance was more frequent for patients enrolled after the COVID-19 pandemic, 71% versus 58%. The opposite effect was seen in the team arm; 61% before COVID and 3% after. Secondary outcomes included the PROMIS Anxiety and Depression scores; these were near established population means (49.8, for clinician and 50.8 for team arm) and did not changes from initial to 6 months. Patients reported high levels of goal concordant care 6 months after engaging in ACP. Differences between the team and clinician arm were not statistically significant, but outcomes were slightly better for the clinician arm. The COVID-19 pandemic may have disrupted team function, impacting the results. [ABSTRACT FROM AUTHOR]

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. Prospective, observational, population-based 16-year follow-up study of nationally representative sample. 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). 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. 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]. 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 [ABSTRACT FROM AUTHOR]

Objective To assess problem list completeness using an objective measure across a range of sites, and to identify success factors for problem list completeness. Methods We conducted a retrospective analysis of electronic health record data and interviews at ten healthcare organizations within the United States, United Kingdom, and Argentina who use a variety of electronic health record systems: four self-developed and six commercial. At each site, we assessed the proportion of patients who have diabetes recorded on their problem list out of all patients with a hemoglobin A1c elevation > = 7.0%, which is diagnostic of diabetes. We then conducted interviews with informatics leaders at the four highest performing sites to determine factors associated with success. Finally, we surveyed all the sites about common practices implemented at the top performing sites to determine whether there was an association between problem list management practices and problem list completeness. Results Problem list completeness across the ten sites ranged from 60.2% to 99.4%, with a mean of 78.2%. Financial incentives, problem-oriented charting, gap reporting, shared responsibility, links to billing codes, and organizational culture were identified as success factors at the four hospitals with problem list completeness at or near 90.0%. Discussion Incomplete problem lists represent a global data integrity problem that could compromise quality of care and put patients at risk. There was a wide range of problem list completeness across the healthcare facilities. Nevertheless, some facilities have achieved high levels of problem list completeness, and it is important to better understand the factors that contribute to success to improve patient safety. Conclusion Problem list completeness varies substantially across healthcare facilities. In our review of EHR systems at ten healthcare facilities, we identified six success factors which may be useful for healthcare organizations seeking to improve the quality of their problem list documentation: financial incentives, problem oriented charting, gap reporting, shared responsibility, links to billing codes, and organizational culture. [ABSTRACT FROM AUTHOR]

We conducted a meta-synthesis of five different studies that developed, tested, and implemented new technologies for the purpose of collecting observations of daily living (ODL). From this synthesis, we developed a model to explain user motivation as it relates to ODL collection. We describe this model that includes six factors that motivate patients' collection of ODL data: usability, illness experience, relevance of ODL, information technology infrastructure, degree of burden, and emotional activation. We show how these factors can act as barriers or facilitators to the collection of ODL data and how interacting with care professionals and sharing ODL data may also influence ODL collection, health-related awareness, and behavior change. The model we developed and used to explain ODL collection can be helpful to researchers and designers who study and develop new, personal health technologies to empower people to improve their health. [ABSTRACT FROM AUTHOR]

Background: Red blood cell transfusion is the most common procedure in hospitalized patients in the US. Growing evidence suggests that a sizeable percentage of these transfusions are inappropriate, putting patients at significant risk and increasing costs to the health care system.Methods: We performed a retrospective quasi-experimental study from November 2008 until November 2014 in a 576-bed tertiary care hospital. The intervention consisted of an interruptive clinical decision support alert shown to a provider when a red blood cell transfusion was ordered in a patient whose most recent hematocrit was ≥21%. We used interrupted time series analysis to determine whether our primary outcome of interest, rate of red blood cell transfusion in patients with hematocrit ≥21% per 100 patient (pt) days, was reduced by the implementation of the clinical decision support tool. The rate of platelet transfusions was used as a nonequivalent dependent control variable.Results: A total of 143,000 hospital admissions were included in our analysis. Red blood cell transfusions decreased from 9.4 to 7.8 per 100 pt days after the clinical decision support intervention was implemented. Interrupted time series analysis showed that significant decline of 0.05 (95% confidence interval [CI], 0.03-0.07; P < .001) units of red blood cells transfused per 100 pt days per month was already underway in the preintervention period. This trend accelerated to 0.1 (95% CI, 0.09-0.12; P < .001) units of red blood cells transfused per 100 pt days per month following the implementation of the clinical decision support tool. There was no statistical change in the rate of platelet transfusion resulting from the intervention.Conclusions: The implementation of an evidence-based clinical decision support tool was associated with a significant decline in the overuse of red blood cell transfusion. We believe this intervention could be easily replicated in other hospitals using commercial electronic health records and a similar reduction in overuse of red blood cell transfusions achieved. [ABSTRACT FROM AUTHOR]

Competing Interests: Disclosures: Disclosure forms are available with the article online.

Background: The impact of multimorbidity (≥ 2 chronic diseases) on the well-being of older adults is substantial but variable. The burden of multimorbidity varies by the number and kinds of conditions, and timing of onset. The impact varies by age, race, ethnicity, socioeconomic status, and health indicators. Large scale longitudinal surveys linked to medical claims provide unique opportunities to characterize this variability., Methods: We analyzed Medicare-linked Health and Retirement Study data for respondents 65 and older with 3 or more years of fee-for-service coverage (n = 17,199; 2000-2016). We applied standardized claims algorithms for operationalizing 21 chronic diseases. We compared multimorbidity levels, demographics, and outcomes at baseline and over time and escalation to high multimorbidity levels (≥ 5 conditions)., Results: At baseline, 51.2% had no multimorbidity, 36.5% had multimorbidity, and 12.4% had high multimorbidity. Loss of function, cognitive decline, and higher healthcare utilization were up to ten times more prevalent in the high multimorbidity group. Greater rates of high multimorbidity were seen among non-Hispanic Black and Hispanic groups, those with lower wealth, younger birth cohorts, and adults with obesity. Rates of transition to high multimorbidity varied greatly and was highest among Hispanic and respondents with lower education., Conclusions: The development and progression of multimorbidity in old age is influenced by many factors. Higher levels of multimorbidity are associated with sociodemographic characteristics, suggesting possible mitigation strategies., (© 2024. The Author(s).)

The US health care delivery system does not systematically engage or support family or friend care partners. Meanwhile, the uptake and familiarity of portals to personal health information are increasing among patients. Technology innovations, such as shared access to the portal, use separate identity credentials to differentiate between patients and care partners. Although not well-known, or commonly used, shared access allows patients to identify who they do and do not want to be involved in their care. However, the processes for patients to grant shared access to portals are often limited or so onerous that interested patients and care partners often circumvent the process entirely. As a result, the vast majority of care partners resort to accessing portals using a patient's identity credentials-a "do-it-yourself" solution in conflict with a health systems' legal responsibility to protect patient privacy and autonomy. The personal narratives in this viewpoint (shared by permission) elaborate on quantitative studies and provide first-person snapshots of challenges faced by patients and families as they attempt to gain or grant shared access during crucial moments in their lives. As digital modalities increase patient roles in health care interactions, so does the importance of making shared access work for all stakeholders involved-patients, clinicians, and care partners. Electronic health record vendors must recognize that both patients and care partners are important users of their products, and health care organizations must acknowledge and support the critical contributions of care partners as distinct from patients., (©Liz Salmi, Danielle Peereboom, David A Dorr, Leilani R Graham, Jennifer L Wolff, Catherine M DesRoches. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.06.2024.)

Objectives: Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility., Materials and Methods: We engaged a convenience sample of informatics leaders from CTSA hubs (n = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts., Results: EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science., Discussion: As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied., Conclusion: This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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., (©2023 AMIA - All rights reserved.)

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., Competing Interests: Competing interests: This study is undertaken within Observational Health Data Sciences and Informatics, an open collaboration. RK received support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (under award K23HL153775) and the Doris Duke Charitable Foundation (under award, 2022060). He is an Associate Editor of JAMA. He also receives research support, through Yale, from Bristol-Myers Squibb. He is a coinventor of US Provisional Patent Applications 63/177,117 and 63/346,610, unrelated to current work. He is also a founder of Evidence2Health, a precision health platform to improve evidence-based cardiovascular care. TD-S acknowledges receiving financial support from the Instituto de Salud Carlos III (ISCIII; Miguel Servet 2021: CP21/00023). KKCM reports grants from C W Maplethorpe Fellowship, grants from National Institute of Health Research, UK, grants from European Commission Framwork Horizon 2020, grants from Hong Kong Research Grant Council, grants from Innovation and Technology Commission of the Hong Kong Special Administration Region Government, and personal fees from IQVIA outside of the submitted work. DRM was supported by a Wellcome Trust Clinical Research Fellowship (214588/Z/18/Z). MJS is an employee and shareholder of Johnson & Johnson. HK received expenses and/or personal fees from UnitedHealth, Element Science, Aetna, Reality Labs, Tesseract/4Catalyst, F-Prime, the Siegfried and Jensen Law Firm, Arnold and Porter Law Firm, and Martin/Baughman Law Firm. He is a co-founder of Refactor Health and HugoHealth, and is associated with contracts, through Yale New Haven Hospital, from the Centers for Medicare and Medicaid Services and through Yale University from Johnson & Johnson. MAS receives contracts and grants from the National Institutes of Health, the US Department of Veterans Affairs, the US Food and Drug Administration and Janssen Research and Development, the latter two of which are outside the scope of this work. Other authors declare no competing interests., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

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., Competing Interests: We have no conflicts of interest or financial interests to report.

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., Competing Interests: The authors have no conflicts of interest relevant to the topic of this manuscript., (© The Author(s) 2023.)

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., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

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., Competing Interests: None declared., (Thieme. All rights reserved.)

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., (©Lily Cook, Juan Espinoza, Nicole G Weiskopf, Nisha Mathews, David A Dorr, Kelly L Gonzales, Adam Wilcox, Charisse Madlock-Brown, N3C Consortium. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 06.09.2022.)

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., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

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.

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., (©2022 AMIA - All rights reserved.)

Objective: To assess and compare electronic health record (EHR) documentation of chronic disease in problem lists and encounter diagnosis records among Community Health Center (CHC) patients., Materials and Methods: We assessed patient EHR data in a large clinical research network during 2012-2019. We included CHCs who provided outpatient, older adult primary care to patients age ≥45 years, with ≥2 office visits during the study. Our study sample included 1 180 290 patients from 545 CHCs across 22 states. We used diagnosis codes from 39 Chronic Condition Warehouse algorithms to identify chronic conditions from encounter diagnoses only and compared against problem list records. We measured correspondence including agreement, kappa, prevalence index, bias index, and prevalence-adjusted bias-adjusted kappa., Results: Overlap of encounter diagnosis and problem list ascertainment was 59.4% among chronic conditions identified, with 12.2% of conditions identified only in encounters and 28.4% identified only in problem lists. Rates of coidentification varied by condition from 7.1% to 84.4%. Greatest agreement was found in diabetes (84.4%), HIV (78.1%), and hypertension (74.7%). Sixteen conditions had <50% agreement, including cancers and substance use disorders. Overlap for mental health conditions ranged from 47.4% for anxiety to 59.8% for depression., Discussion: Agreement between the 2 sources varied substantially. Conditions requiring regular management in primary care settings may have a higher agreement than those diagnosed and treated in specialty care., Conclusion: Relying on EHR encounter data to identify chronic conditions without reference to patient problem lists may under-capture conditions among CHC patients in the United States., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD., Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test , 886,193 hospitalized with COVID-19 , and 113,627 hospitalized with COVID-19 requiring intensive services ., Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed., Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance., Competing Interests: Ms. Kostka was an employee of IQVIA during the conduct of this study and received grant funding from the NIH NCATS National COVID Cohort Collaborative and the Bill and Melinda Gates Foundation. Mr. Sena is an employee and holds stock at Janssen Research & Development, a Johnson and Johnson family of companies. Dr. Golozar reports personal fees from Regeneron Pharmaceuticals, outside the submitted work. She is a full-time employee at Regeneron Pharmaceuticals. This work was not conducted at Regeneron Pharmaceuticals. Dr. Nyberg was an employee of AstraZeneca until 2019 and hold some shares. Dr. Wilcox reports grants from Bill and Melinda Gates Foundation, grants from National Institute of Health, during the conduct of the study. Mr. Andryc is an employee of Janssen Research & Development, a subsidiary of Johnson & Johnson. Dr. Reich is an employee of IQVIA. Dr. Blacketer reports she is an employee and holds stock at Janssen Research & Development, a Johnson and Johnson family of companies. Dr. Morales is supported by a Wellcome Trust Clinical Research Development Fellowship (Grant 214588/Z/18/Z) and reports grants from Chief Scientist Office (CSO), grants from Health Data Research UK (HDR-UK), grants from National Institute of Health Research (NIHR), outside the submitted work. Mr. DeFalco reports he is an employee and holds stock at Janssen Research & Development, a Johnson and Johnson family of companies. Mr. Thomas reports grants from Bill and Melinda Gates Foundation (INV-016910), grants from National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number UL1TR002369 to his institution, during the conduct of the study. Dr Jiang Bian reports grants from NIH/NIEHS (R21ES032762), during the conduct of the study. Dr. Posada reports grants from National Library of Medicine, during the conduct of the study. Dr. Natarajan reports grants from US NIH, during the conduct of the study. Dr. Matheny reports grants from US NIH, grants from US VA HSR&D, during the conduct of the study. Dr. Weiskopf reports personal fees from Merck, during the conduct of the study and outside the submitted work. Dr. Shah reports grants from National Library of Medicine, during the conduct of the study. Dr. Park reports grants from Ministry of Trade, Industry & Energy, Republic of Korea, grants from Ministry of Health & Welfare, Republic of Korea, grants from Bill & Melinda Gates Foundation, during the conduct of the study. Mr Robert Schuff reports grants from Gates Foundation, grants from NIH-NCATS, during the conduct of the study. Ms. Seager is an employee of IQVIA. Dr. DuVall reports grants from Anolinx, LLC, Astellas Pharma, Inc, AstraZeneca Pharmaceuticals LP, Boehringer Ingelheim International GmbH, Celgene Corporation, Eli Lilly and Company, Genentech Inc., Genomic Health, Inc., Gilead Sciences Inc., GlaxoSmithKline PLC, Innocrin Pharmaceuticals Inc., Janssen Pharmaceuticals, Inc., Kantar Health, Myriad Genetic Laboratories, Inc., Novartis International AG, and Parexel International Corporation through the University of Utah or Western Institute for Veteran Research outside the submitted work. Dr. Fortin is an employee of Janssen R&D, a subsidiary of Johnson and Johnson. Dr. Subbian reports grants from State of Arizona; Arizona Board of Regents, during the conduct of the study; grants from National Science Foundation (grant# 1838745), grants from Agency for Healthcare Research and Quality, grants from National Institutes of Health, outside the submitted work. Dr. Rijnbeek reports grants from Innovative Medicines Initiative, Janssen Research and Development, during the conduct of the study. He also works for a research institute which receives/received unconditional research grants from Yamanouchi, Pfizer-Boehringer Ingelheim, GSK, Amgen, UCB, Novartis, Astra-Zeneca, Chiesi, Janssen Research and Development, none of which relate to the content of this work. Dr. Hripcsak reports grants from US NIH and Janssen Research. Dr. Ryan is an employee of Janssen Research and Development and shareholder of Johnson & Johnson. Dr. Suchard reports grants from US National Institutes of Health, Department of Veterans Affairs, during the conduct of the study; grants and/or personal fees from IQVIA, Janssen Research and Development, US Food and Drug Administration, and Private Health Management, outside the submitted work. Dr. Prieto-Alhambra reports grants, non-financial support, speaker/consultancy services and/or advisory board membership from AMGEN, UCB Biopharma, and Les Laboratoires Servier, outside the submitted work; and Janssen, on behalf of IMI-funded EHDEN and EMIF consortiums, and Synapse Management Partners have supported training programmes organised by DPA’s Department and open for external participants. The views expressed are those of the authors and do not necessarily represent the views or policy of the Department of Veterans Affairs or the United States Government. No other relationships or activities that could appear to have influenced the submitted work. The authors report no other conflicts of interest in this work., (© 2022 Kostka et al.)

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., Competing Interests: None declared., (Thieme. All rights reserved.)

Background: Primary Care Medical Home (PCMH) redesign efforts are intended to enhance primary care's ability to improve population health and well-being. PCMH transformation that is focused on "high-value elements" (HVEs) for cost and utilization may improve effectiveness., Objectives: The objective of this study was to determine if a focus on achieving HVEs extracted from successful primary care transformation models would reduce cost and utilization as compared with a focus on achieving PCMH quality improvement goals., Research Design: A stratified, cluster randomized controlled trial with 2 arms. All practices received equal financial incentives, health information technology support, and in-person practice facilitation. Analyses consisted of multivariable modeling, adjusting for the cluster, with difference-in-difference results., Subjects: Eight primary care clinics that were engaged in PCMH reform., Measures: We examined: (1) total claims payments; (2) emergency department (ED) visits; and (3) hospitalizations among patients during baseline and intervention years., Results: In total, 16,099 patients met the inclusion criteria. Intervention clinics had significantly lower baseline ED visits (P=0.02) and claims paid (P=0.01). Difference-in-difference showed a decrease in ED visits greater in control than intervention (ED per 1000 patients: +56; 95% confidence interval: +96, +15) with a trend towards decreased hospitalizations in intervention (-15; 95% confidence interval: -52, +21). Costs were not different. In modeling monthly outcome means, the generalized linear mixed model showed significant differences for hospitalizations during the intervention year (P=0.03)., Discussion: The trial had a trend of decreasing hospitalizations, increased ED visits, and no change in costs in the HVE versus quality improvement arms., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence., Competing Interests: None of the authors have any conflicts of interest to report nor received any funding in support of the manuscript development., (© 2021 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.)

Objective: This study examines guideline-based high blood pressure (HBP) and hypertension recommendations and evaluates the suitability and adequacy of the data and logic required for a Fast Healthcare Interoperable Resources (FHIR)-based, patient-facing clinical decision support (CDS) HBP application. HBP is a major predictor of adverse health events, including stroke, myocardial infarction, and kidney disease. Multiple guidelines recommend interventions to lower blood pressure, but implementation requires patient-centered approaches, including patient-facing CDS tools., Methods: We defined concept sets needed to measure adherence to 71 recommendations drawn from eight HBP guidelines. We measured data quality for these concepts for two cohorts (HBP screening and HBP diagnosed) from electronic health record (EHR) data, including four use cases (screening, nonpharmacologic interventions, pharmacologic interventions, and adverse events) for CDS., Results: We identified 102,443 people with diagnosed and 58,990 with undiagnosed HBP. We found that 21/35 (60%) of required concept sets were unused or inaccurate, with only 259 (25.3%) of 1,101 codes used. Use cases showed high inclusion (0.9-11.2%), low exclusion (0-0.1%), and missing patient-specific context (up to 65.6%), leading to data in 2/4 use cases being insufficient for accurate alerting., Discussion: Data quality from the EHR required to implement recommendations for HBP is highly inconsistent, reflecting a fragmented health care system and incomplete implementation of standard terminologies and workflows. Although imperfect, data were deemed adequate for two test use cases., Conclusion: Current data quality allows for further development of patient-facing FHIR HBP tools, but extensive validation and testing is required to assure precision and avoid unintended consequences., Competing Interests: None declared., (Thieme. All rights reserved.)

This editorial provides context for a series of published case reports in ACI Open by summarizing activities and outputs of joint electronic health record integration and pharmacogenomics workgroups in the NIH-funded electronic Medical Records and Genomics (eMERGE) Network. A case report is a useful tool to describe the range of capabilities that an IT infrastructure or a particular technology must support. The activities we describe have informed infrastructure requirements used during eMERGE phase III, provided a venue to share experiences and ask questions among other eMERGE sites, summarized potential hazards that might be encountered for specific clinical decision support (CDS) implementation scenarios, and provided a simple framework that captured progress toward implementing CDS at eMERGE sites in a consistent format., Competing Interests: Conflict of Interest None declared.

Background: Patients with complex health care needs may suffer adverse outcomes from fragmented and delayed care, reducing well-being and increasing health care costs. Health reform efforts, especially those in primary care, attempt to mitigate risk of adverse outcomes by better targeting resources to those most in need. However, predicting who is susceptible to adverse outcomes, such as unplanned hospitalizations, ED visits, or other potentially avoidable expenditures, can be difficult, and providing intensive levels of resources to all patients is neither wanted nor efficient. Our objective was to understand if primary care teams can predict patient risk better than standard risk scores., Methods: Six primary care practices risk stratified their entire patient population over a 2-year period, and worked to mitigate risk for those at high risk through care management and coordination. Individual patient risk scores created by the practices were collected and compared to a common risk score (Hierarchical Condition Categories) in their ability to predict future expenditures, ED visits, and hospitalizations. Accuracy of predictions, sensitivity, positive predictive values (PPV), and c-statistics were calculated for each risk scoring type. Analyses were stratified by whether the practice used intuition alone, an algorithm alone, or adjudicated an algorithmic risk score., Results: In all, 40,342 patients were risk stratified. Practice scores had 38.6% agreement with HCC scores on identification of high-risk patients. For the 3,381 patients with reliable outcomes data, accuracy was high (0.71-0.88) but sensitivity and PPV were low (0.16-0.40). Practice-created scores had 0.02-0.14 lower sensitivity, specificity and PPV compared to HCC in prediction of outcomes. Practices using adjudication had, on average, .16 higher sensitivity., Conclusions: Practices using simple risk stratification techniques had slightly worse accuracy in predicting common outcomes than HCC, but adjudication improved prediction.

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Structured electronic health record (EHR) data are often used for quality measurement and improvement, clinical research, and other secondary uses. These data, however, are known to suffer from quality problems. There may be value in augmenting structured EHR data to improve data quality, thereby improving the reliability and validity of the conclusions drawn from those data. Focusing on five diagnoses related to cardiovascular care, this paper considers the added value of two alternative data sources: manual chart abstraction and patient self-report. We assess the overall agreement between structured EHR problem list data, abstracted EHR data, and patient self- report; and explore possible causes of disagreement between those sources. Our findings suggest that both chart abstraction and patient self-report contain significantly more diagnoses than the problem list, but that the information they capture is different. Methods for collecting and validating self-reported medical data require further consideration and exploration., (©2019 AMIA - All rights reserved.)

Burnout is becoming increasingly prevalent among primary care physicians. Recent studies have attributed electronic health record (EHR) related tasks as a potential source of physician burnout. In this cross-sectional study, EHR use was compared to self-reported burnout for 107 faculty physicians at 10 university-affiliated primary care clinics. Physicians who self-reported burnout spent more time managing their inbox and in the EHR after hours. Burnout was associated with lower rates of same day chart closure, longer completion time for inbox messages, and more incomplete messages. Burnout, while related to overall workload, had a complex relationship with EHR use, which was influenced by but could not wholly explained by clinical workload. Our results suggest that burnout is less prevalent with less allocated clinical time, however, more research is necessary to identify the optimal balance between clinical duties and academic pursuits. Segmenting providers based on relative workload to determine variation within similar groups may help optimize EHR use.

Clinical quality measures (CQMs) aim to identify gaps in care and to promote evidence-based guidelines. Official CQM definitions consist of a measure's logic and grouped, standardized codes to define key concepts. In this study, we used the official CQM update process to understand how CQMs' meanings change over time. First, we identified differences between the narrative description, logic, and the vocabulary specifications offour standardized CQMs' definitions in subsequent versions (2015, 2016, and 2017). Next, we implemented the various versions in a quality measure calculation registry to understand how the differences affected calculated prevalence of risk and measure performance. Global performance rates changed up to 5.32%, and an increase of up to 28% new patients was observed for key conditions between versions. Updates to definitions that change a measure's logic and choices to include/exclude codes in value set vocabularies changes measurement of quality and likely introduces variation by implementation.

Failed diffusion of innovations may be linked to an inability to use and apply data, information, and knowledge to change perceptions of current practice and motivate change. Using qualitative and quantitative data from three large-scale health care delivery innovations-accountable care organizations, advanced primary care practice, and EvidenceNOW-we assessed where data-driven innovation is occurring and where challenges lie. We found that implementation of some technological components of innovation (for example, electronic health records) has occurred among health care organizations, but core functions needed to use data to drive innovation are lacking. Deficits include the inability to extract and aggregate data from the records; gaps in sharing data; and challenges in adopting advanced data functions, particularly those related to timely reporting of performance data. The unexpectedly high costs and burden incurred during implementation of the innovations have limited organizations' ability to address these and other deficits. Solutions that could help speed progress in data-driven innovation include facilitating peer-to-peer technical assistance, providing tailored feedback reports to providers from data aggregators, and using practice facilitators skilled in using data technology for quality improvement to help practices transform. Policy efforts that promote these solutions may enable more rapid uptake of and successful participation in innovative delivery system reforms.

Objectives: Determine if clinical decision support (CDS) malfunctions occur in a commercial electronic health record (EHR) system, characterize their pathways and describe methods of detection., Methods: We retrospectively examined the firing rate for 226 alert type CDS rules for detection of anomalies using both expert visualization and statistical process control (SPC) methods over a five year period. Candidate anomalies were investigated and validated., Results: Twenty-one candidate CDS anomalies were identified from 8,300 alert-months. Of these candidate anomalies, four were confirmed as CDS malfunctions, eight as false-positives, and nine could not be classified. The four CDS malfunctions were a result of errors in knowledge management: 1) inadvertent addition and removal of a medication code to the electronic formulary list; 2) a seasonal alert which was not activated; 3) a change in the base data structures; and 4) direct editing of an alert related to its medications. 154 CDS rules (68%) were amenable to SPC methods and the test characteristics were calculated as a sensitivity of 95%, positive predictive value of 29% and F-measure 0.44., Discussion: CDS malfunctions were found to occur in our EHR. All of the pathways for these malfunctions can be described as knowledge management errors. Expert visualization is a robust method of detection, but is resource intensive. SPC-based methods, when applicable, perform reasonably well retrospectively., Conclusion: CDS anomalies were found to occur in a commercial EHR and visual detection along with SPC analysis represents promising methods of malfunction detection.

Objective: To understand the impact of distinct concept to value set mapping on the measurement of quality of care., Background: Clinical quality measures (CQMs) intend to measure the quality of healthcare services provided, and to help promote evidence-based therapies. Most CQMs consist of grouped codes from vocabularies - or 'value sets' - that represent the unique identifiers (i.e., object identifiers), concepts (i.e., value set names), and concept definitions (i.e., code groups) that define a measure's specifications. In the development of a statin therapy CQM, two unique value sets were created by independent measure developers for the same global concepts., Methods: We first identified differences between the two value set specifications of the same CQM. We then implemented the various versions in a quality measure calculation registry to understand how the differences affected calculated prevalence of risk and measure performance., Results: Global performance rates only differed by 0.8%, but there were up to 2.3 times as many patients included with key conditions, and differing performance rates of 7.5% for patients with 'myocardial infarction' and 3.5% for those with 'ischemic vascular disease'., Conclusion: The decisions CQM developers make about which concepts and code groups to include or exclude in value set vocabularies can lead to inaccuracies in the measurement of quality of care. One solution is that developers could provide rationale for these decisions. Endorsements are needed to encourage system vendors, payers, informaticians, and clinicians to collaborate in the creation of more integrated terminology sets.

Clinical quality measures (CQMs) are important tools for the assessment and improvement of health care quality. Federal requirements initially set forth in the American Recovery and Reinvestment Act, and advanced in subsequent stages of the requirements, codified electronic health record (EHR)-based CQM reporting, and have made automated CQM implementation a priority amongst the clinical and informatics communities. Nevertheless, the processes surrounding CQM implementation and validation remain complex, time-consuming, and largely undefined. We collected issue-tracking data during the course of an agile and rigorous collaborative project to build an analytics platform for the Knight Cardiovascular Institute at OHSU, with nine heart failure CQMs defined by the American College of Cardiology (ACC) as an exemplar. Using a mixed methods approach we provide an overview of our CQM implementation and validation process, identify major roadblocks and bottlenecks, and make recommendations for other professionals working in the area of health care quality assessment and improvement.

Background: Health reform programs like the patient-centered medical home are intended to improve the triple aim. Previous studies on patient-centered medical homes have shown mixed effects, but high value elements (HVEs) are expected to improve the triple aim., Objective: The aim of this study is to understand whether focusing on HVEs would improve patient experience with care., Methods: Eight clinics were cluster-randomized in a year-long trial. Both arms received practice facilitation, IT-based reporting, and financial incentives. Intervention practices were encouraged to choose HVEs for quality improvement goals. To assess patient experience, 1597 Consumer Assessment of Healthcare Providers and Systems surveys were sent pretrial and posttrial to a stratified random sample of patients. Difference-in-difference multivariate analysis was used to compare patient responses from intervention and control practices, adjusting for confounders., Results: The response rate was 43% (n=686). Nonrespondent analysis showed no difference between arms, although differences were seen by risk status and age. The overall difference in difference was 2.8%, favoring the intervention. The intervention performed better in 9 of 11 composites. The intervention performed significantly better in follow-up on test results (P=0.091) and patients' rating of the provider (P=0.091), whereas the control performed better in access to care (P=0.093). Both arms also had decreases, including 4 of 11 composites for the intervention, and 8 of 11 for the control., Discussion: Practices that targeted HVEs showed significantly more improvement in patient experience of care. However, contemporaneous trends may have affected results, leading to declines in patient experience in both arms.

Objective: Examine how the Electronic Health Record (EHR) and its related systems support or inhibit provider collaboration., Background: Health care systems in the US are simultaneously implementing EHRs and transitioning to more collaborative delivery systems; this study examines the interaction between these two changes., Methods: This qualitative study of five US EHR implementations included 49 interviews and over 60 hours of provider observation. We examined the role of the EHR in building relationships, communicating, coordinating, and collaborative decision-making., Results: The EHR plays four roles in collaboration: a repository, a messenger, an orchestrator, and a monitor. While EHR performance varied, common themes were decreased trust due to poor quality documentation, incomplete communication, potential for increased effectiveness through better coordination, and the emerging role of the EHR in identifying performance gaps., Conclusion: Both organizational and technical innovations are needed if the EHR is to truly support collaborative behaviors.

Background: Care management has demonstrated improvements in quality of care for patients with complex care needs. The extent to which these interventions benefit race/ethnic minority populations is unclear., Objectives: To characterize race/ethnic differences in the longitudinal control of clinical outcomes for patients with complex care needs enrolled in Care Management Plus, a health information technology-enabled care coordination intervention., Research Design: Multilevel models of repeated observations from clinical encounters before and after program enrollment for 6 Oregon and California primary care clinics., Subjects: A total of 18,675 clinic patients were examined. We estimated multilevel models for 1481 and 5320 care-managed individuals with repeated hemoglobin A1c and blood pressure measurements, respectively., Measures: Primary outcomes were changes over time for 2 clinical markers of health status for complex care patients: (1) hemoglobin A1c for patients with diabetes; and (2) mid-blood pressure (BP) (average systolic and diastolic blood pressure)., Results: We found significant reductions in A1c for patients with previously uncontrolled A1c (preperiod slope, b=1.03 [0.83, 1.24]; postperiod slope, b=-0.63 [-0.91, -0.35]). For mid-BP we found increasing unconditional preperiod trajectories (b=3.52 [2.39, 4.64]) and decreasing postperiod trajectories (b=-5.21 [-5.70, -4.72]). We also found the trajectories of A1c and mid-BP were not statistically different for black, Latino, and white patients., Conclusions: These analyses demonstrate some promising results for intermediate clinical outcomes for underrepresented patients with complex chronic care needs. It remains to be seen whether these health care system delivery redesigns yield long-term benefits for patients, such as improvements in function and quality of life.

Care coordination is at the forefront of current health reform efforts, yet most electronic health records (EHRs) lack the functionality needed to facilitate and document care coordination activities. The Integrated Care Coordination Information System (ICCIS) was iteratively developed with user input to meet these needs. Following 16 months of system use, ICCIS users and developers were interviewed about their experiences. These interviews, along with quantitative information about system use, were analyzed using a combination of Linstone's Multiple Perspective approach and the ABC framework to determine lessons learned about novel system creation. Overall, clinicians saw value in specialized health information technology (HIT) tools for care coordination as long as development focuses on providing user-requested functionality that integrates closely with existing HIT systems and workflows. Close integration between novel HIT and EHRs may increase use by relieving the cited fatigues of duplicative data entry, multiple system logins, and potential data inconsistencies.

Quality measurement has been slow to make a major impact in health care. Initial measures were too simple to affect outcomes of importance. Incentive programs such as Meaningful Use encourage better measures, but in process may become more complex. We evaluated the measures selected for Meaningful Use in two ways: we counted unique concept identifiers, taxonomies, and aggregated concepts as measures of complexity; and we surveyed informatics professionals to assess difficulty. There were 20,316 unique concept identifiers, 35 taxonomies, and 317 aggregated concepts across the 45 measures. Half the respondents reported measures at least moderately difficult. The number of identifiers was associated with fewer implementations (r=-.37); rating-of-difficulty was associated with more taxonomies (r=.24). The impact on accuracy may be substantial when moving to measures intended to be more relevant to clinical outcomes but requiring the use of more taxonomies, unused structured concept identifiers, or concepts only in free text fields.

The goal of this study is to describe a framework that allows decision makers to efficiently evaluate factors that affect Electronic Health Record (EHR) adoption and test suitable interventions; specifically financial incentives. The United States healthcare delivery system is experiencing a transformation to improve population health. There is strong agreement that "meaningful use" of Health Information Technology (HIT) is a major enabler in this effort. However it's also understood that the high cost of implementing an EHR is an obstacle for adoption. To help understand these complexities we developed a simulation model designed to capture the dynamic nature of policy interventions that affect the adoption of EHR. We found that "Effective" use of HIT approaches break-even-point and larger clinic revenue many times faster that "average" or "poor" use of HIT. This study uses a systems perspective to the evaluate EHR adoption process through the "meaningful use" redesign as proposed in the American Reinvestment and Recovery Act 2009 in the United States healthcare industry by utilizing the System Dynamics methodology and Scenario Analysis.

We present the User Need Analysis process for the Integrated Care Coordination Information System (ICCIS) research project at Oregon Health & Science University (OHSU). The primary goal of the process was to select a mix of methodologies to perform user need analysis which would lead to generation of system requirements. The ICCIS Team developers would use these prioritized requirements to build the next generation of the ICCIS software that would help meet the overall goals of the care model. First, semi-structured User Discussion Guides and structured questioners were used to collect raw needs from Physicians, Nurses, IT and Administrative personal. Later Conjoint Analysis (CA) and Dimensional Analysis (DA) were used to generate emerging concepts and priorities from the discussions with the end-user community.