Identification and assessment of classification criteria attributes for systemic lupus erythematosus in a regional medical record data network.

OBJECTIVE: To assess the application and utility of algorithms designed to detect features of SLE in electronic health record (EHR) data in a multisite, urban data network. METHODS: Using the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN), a Clinical Data Research Network (CDRN) containing data from multiple healthcare sites, we identified patients with at least one positively identified criterion from three SLE classification criteria sets developed by the American College of Rheumatology (ACR) in 1997, the Systemic Lupus International Collaborating Clinics (SLICC) in 2012, and the European Alliance of Associations for Rheumatology and the ACR in 2019 using EHR-based algorithms. To measure the algorithms' performance in this data setting, we first evaluated whether the number of clinical encounters for SLE was associated with a greater quantity of positively identified criteria domains using Poisson regression. We next quantified the amount of SLE criteria identified at a single healthcare institution versus all sites to assess the amount of SLE-related information gained from implementing the algorithms in a CDRN. RESULTS: Patients with three or more SLE encounters were estimated to have documented 2.77 (2.73 to 2.80) times the number of positive SLE attributes from the 2012 SLICC criteria set than patients without an SLE encounter via Poisson regression. Patients with three or more SLE-related encounters and with documented care from multiple institutions were identified with more SLICC criteria domains when data were included from all CAPriCORN sites compared with a single site (p<0.05). CONCLUSIONS: The positive association observed between amount of SLE-related clinical encounters and the number of criteria domains detected suggests that the algorithms used in this study can be used to help describe SLE features in this data environment. This work also demonstrates the benefit of aggregating data across healthcare institutions for patients with fragmented care.

Differences in Health Professionals' Engagement With Electronic Health Records Based on Inpatient Race and Ethnicity.

Importance: US health professionals devote a large amount of effort to engaging with patients' electronic health records (EHRs) to deliver care. It is unknown whether patients with different racial and ethnic backgrounds receive equal EHR engagement. Objective: To investigate whether there are differences in the level of health professionals' EHR engagement for hospitalized patients according to race or ethnicity during inpatient care. Design, Setting, and Participants: This cross-sectional study analyzed EHR access log data from 2 major medical institutions, Vanderbilt University Medical Center (VUMC) and Northwestern Medicine (NW Medicine), over a 3-year period from January 1, 2018, to December 31, 2020. The study included all adult patients (aged ≥18 years) who were discharged alive after hospitalization for at least 24 hours. The data were analyzed between August 15, 2022, and March 15, 2023. Exposures: The actions of health professionals in each patient's EHR were based on EHR access log data. Covariates included patients' demographic information, socioeconomic characteristics, and comorbidities. Main Outcomes and Measures: The primary outcome was the quantity of EHR engagement, as defined by the average number of EHR actions performed by health professionals within a patient's EHR per hour during the patient's hospital stay. Proportional odds logistic regression was applied based on outcome quartiles. Results: A total of 243 416 adult patients were included from VUMC (mean [SD] age, 51.7 [19.2] years; 54.9% female and 45.1% male; 14.8% Black, 4.9% Hispanic, 77.7% White, and 2.6% other races and ethnicities) and NW Medicine (mean [SD] age, 52.8 [20.6] years; 65.2% female and 34.8% male; 11.7% Black, 12.1% Hispanic, 69.2% White, and 7.0% other races and ethnicities). When combining Black, Hispanic, or other race and ethnicity patients into 1 group, these patients were significantly less likely to receive a higher amount of EHR engagement compared with White patients (adjusted odds ratios, 0.86 [95% CI, 0.83-0.88; P < .001] for VUMC and 0.90 [95% CI, 0.88-0.92; P < .001] for NW Medicine). However, a reduction in this difference was observed from 2018 to 2020. Conclusions and Relevance: In this cross-sectional study of inpatient EHR engagement, the findings highlight differences in how health professionals distribute their efforts to patients' EHRs, as well as a method to measure these differences. Further investigations are needed to determine whether and how EHR engagement differences are correlated with health care outcomes.

Multi-ancestry genome- and phenome-wide association studies of diverticular disease in electronic health records with natural language processing enriched phenotyping algorithm.

OBJECTIVE: Diverticular disease (DD) is one of the most prevalent conditions encountered by gastroenterologists, affecting ~50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with DD, leveraging multiple electronic health record (EHR) data sources of 91,166 multi-ancestry participants with a Natural Language Processing (NLP) technique. MATERIALS AND METHODS: We developed a NLP-enriched phenotyping algorithm that incorporated colonoscopy or abdominal imaging reports to identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of DD in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in clinical phenotypes. RESULTS: Our developed algorithm showed a significant improvement in patient classification performance for DD analysis (algorithm PPVs ≥ 0.94), with up to a 3.5 fold increase in terms of the number of identified patients than the traditional method. Ancestry-stratified analyses of diverticulosis and diverticulitis of the identified subjects replicated the well-established associations between ARHGAP15 loci with DD, showing overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients. Our PheWAS analyses identified significant associations between the DD GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes. DISCUSSION: As the first multi-ancestry GWAS-PheWAS study, we showcased that heterogenous EHR data can be mapped through an integrative analytical pipeline and reveal significant genotype-phenotype associations with clinical interpretation. CONCLUSION: A systematic framework to process unstructured EHR data with NLP could advance a deep and scalable phenotyping for better patient identification and facilitate etiological investigation of a disease with multilayered data.

Is more better? The impact of implementing more interventions for hypertension control in a practice facilitation study for small- and medium-sized practices.

Interventions for blood pressure (BP) control have positive effects on outcomes for patients with hypertension. Research on these effects in small- and medium-sized practices is limited. Our retrospective analysis used data from Healthy Hearts in the Heartland (H3), a research program conducted in 2016-2018 as part of the Agency for Healthcare Research and Quality's EvidenceNOW initiative, to examine the impact of implementing more interventions for BP control in these settings. Thirty-eight H3 practices met inclusion criteria and were assigned to an implementer group (high or low) based on the number of interventions implemented with the support of a practice facilitator during the study. Practices in the high-implementer group implemented a mean of 2.2 additional interventions relative to the low-implementer group. Groups were compared on two measures of BP control: (1) mean percentage of hypertensive patients with a most recent BP below 140/90, and (2) mean systolic and diastolic BP of hypertensive patients. In the first measure, practices in the high-implementer group had greater improvement between baseline and the end of the study. Among the 10,150 patients included in the second measure, reductions in mean SBP and DBP were greater for the high-implementer group. These outcomes show that implementing additional interventions had a positive association with measures of BP control, though clinical significance was unknown or limited. Future research is needed to understand the impact of interventions for BP control in small- and medium-sized practices, including the interactions among intervention implementation, practice facilitation, and practice and patient characteristics.

Comparison of Sodium-Glucose Cotransporter-2 Inhibitor and Glucagon-Like Peptide-1 Receptor Agonist Prescribing in Patients With Diabetes Mellitus With and Without Cardiovascular Disease.

Sodium-glucose cotransporter-2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP1-RAs) reduce cardiovascular events and mortality in patients with type 2 diabetes mellitus (T2DM). We sought to describe trends in prescribing for SGLT2is and GLP1-RAs in diverse care settings, including (1) the outpatient clinics of a midwestern integrated health system and (2) small- and medium-sized community-based primary care practices and health centers in 3 midwestern states. We included adults with T2DM and ≥1 outpatient clinic visit. The outcomes of interest were annual active prescription rates for SGLT2is and GLP1-RAs (separately). In the integrated health system, 22,672 patients met the case definition of T2DM. From 2013 to 2019, the overall prescription rate for SGLT2is increased from 1% to 15% (absolute difference [AD] 14%, 95% confidence interval [CI] 13% to 15%, p <0.01). The GLP1-RA prescription rate was stable at 10% (AD 0%, 95% CI -1% to 1%, p = 0.9). In community-based primary care practices, 43,340 patients met the case definition of T2DM. From 2013 to 2017, the SGLT2i prescription rate increased from 3% to 7% (AD 4%, 95% CI 3% to 6%, p <0.01), whereas the GLP1-RA prescription rate was stable at 2% to 3% (AD 1%, 95% CI -1 to 1%, p = 0.40). In a fully adjusted regression model, non-Hispanic Black patients had lower odds of SGLT2i or GLP1-RA prescription (odds ratio 0.56, 95% CI 0.34 to 0.89, p = 0.016). In conclusion, the increase in prescription rates was greater for SGLT2is than for GLP1-RAs in patients with T2DM in a large integrated medical center and community primary care practices. Overall, prescription rates for eligible patients were low, and racial disparities were observed.

The development and initial validation of the PROMIS®+HF-27 and PROMIS+HF-10 profiles.

AIMS: Heart failure (HF) is a common and morbid condition impacting multiple health domains. We previously reported the development of the PROMIS®-Plus-HF (PROMIS+HF) profile measure, including universal and HF-specific items. To facilitate use, we developed shorter, PROMIS+HF profiles intended for research and clinical use. METHODS AND RESULTS: Candidate items were selected based on psychometric properties and symptom range coverage. HF clinicians (n = 43) rated item importance and clinical actionability. Based on these results, we developed the PROMIS+HF-27 and PROMIS+HF-10 profiles with summary scores (0-100) for overall, physical, mental, and social health. In a cross-sectional sample (n = 600), we measured internal consistency reliability (Cronbach's alpha and Spearman-Brown), test-retest reliability (intraclass coefficient; n = 100), known-groups validity via New York Heart Association (NYHA) class, and convergent validity with Kansas City Cardiomyopathy Questionnaire (KCCQ) scores. In a longitudinal sample (n = 75), we evaluated responsiveness of baseline/follow-up scores by calculating mean differences and Cohen's d and comparing with paired t-tests. Internal consistency was good to excellent (α 0.82-0.94) for all PROMIS+HF-27 scores and acceptable to good (α/Spearman-Brown 0.60-0.85) for PROMIS+HF-10 scores. Test-retest intraclass coefficients were acceptable to excellent (0.75-0.97). Both profiles demonstrated known-groups validity for the overall and physical health summary scores based on NYHA class, and convergent validity for nearly all scores compared with KCCQ scores. In the longitudinal sample, we demonstrated responsiveness for PROMIS+HF-27 and PROMIS+HF-10 overall and physical summary scores. For the PROMIS+HF overall summary scores, a group-based increase of 7.6-8.3 points represented a small to medium change (Cohen's d = 0.40-0.42). For the PROMIS+HF physical summary scores, a group-based increase of 5.0-5.9 points represented a small to medium change (Cohen's d = 0.29-0.35). CONCLUSIONS: The PROMIS+HF-27 and PROMIS+HF-10 profiles demonstrated good psychometric characteristics with evidence of responsiveness for overall and physical health. These new measures can facilitate patient-centred research and clinical care, such as improving care quality through symptom monitoring, facilitating shared decision-making, evaluating quality of care, assessing new interventions, and monitoring during the initiation and titration of guideline-directed medical therapy.

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.

A Cohort Analysis of Statin Treatment Patterns Among Small-Sized Primary Care Practices.

BACKGROUND: Small-sized primary care practices, defined as practices with fewer than 10 clinicians, delivered the majority of outpatient visits in the USA. Statin therapy in high-risk individuals reduces atherosclerotic cardiovascular disease (ASCVD) events, but prescribing patterns in small primary care practices are not well known. This study describes statin treatment patterns in small-sized primary care practices and examines patient- and practice-level factors associated with lack of statin treatment. METHODS: We conducted a retrospective cohort analysis of statin-eligible patients from practices that participated in Healthy Hearts in the Heartland (H3), a quality improvement initiative aimed at improving cardiovascular care measures in small primary care practices. All statin-eligible adults who received care in one of 53 H3 practices from 2013 to 2016. Statin-eligible adults include those aged at least 21 with (1) clinical ASCVD, (2) low-density lipoprotein cholesterol (LDL-C) ≥ 190 mg/dL, or (3) diabetes aged 40-75 and with LDL-C 70-189 mg/dL. Eligible patients with no record of moderate- to high-intensity statin prescription are defined by ACC/AHA guidelines. RESULTS: Among the 13,330 statin-eligible adults, the mean age was 58 years and 52% were women. Overall, there was no record of moderate- to high-intensity statin prescription among 5,780 (43%) patients. Younger age, female sex, and lower LDL-C were independently associated with a lack of appropriate intensity statin therapy. Higher proportions of patients insured by Medicaid and having only family medicine trained physicians (versus having at least one internal medicine trained physician) at the practice were also associated with lower appropriate intensity statin use. Lack of appropriate intensity statin therapy was higher in independent practices than in Federally Qualified Health Centers (FQHCs) (50% vs. 40%, p value < 0.01). CONCLUSIONS: There is an opportunity for improved ASCVD risk reduction in small primary care practices. Statin treatment patterns and factors influencing lack of treatment vary by practice setting, highlighting the importance of tailored approaches to each setting.

Use of real-world evidence data to evaluate the comparative effectiveness of second-line type 2 diabetes medications on chronic kidney disease.

Chronic kidney disease (CKD) is a common complication of type 2 diabetes mellitus (T2DM). Approximately-one-third of patients with T2DM also have CKD. In clinical trial studies, several anti-diabetic medications (ADM) show evidence of preventing the progression of CKD. Biguanides (e.g., metformin) are widely accepted as the first line medication. However, the comparative effectiveness of second line ADMs on CKD outcomes in T2DM is unclear. In addition, results from clinical trials may not generalize into routine clinical practice. In this study, we aimed to investigate the association of second line ADMs with diagnosed incident CKD, CKD hospitalization, and eGFR < 45 mL/min in T2DM patients using real-world data from electronic health records. Our study found that treatment with sodium-glucose cotransporter 2 (SGLT-2) inhibitors was significantly associated with lower risk of diagnosed CKD incidence in both primary analysis (hazard ratio, 0.43; 95 % CI, [0.22;0.87]; p-value,0.02) and secondary analysis (hazard ratio, 0.42; 95 % CI, [0.19;0.92]; p-value, 0.03) compared to use of Sulfonylureas (SU) as a second-line ADM. However, significant associations were not observed when using eGFR < 45 mL/min as the endpoint. Treatment with a dipeptidyl peptidase 4 (DPP-4) inhibitor was significantly associated with lower risk of diagnosed incident CKD (hazard ratio, 0.7; 95 % CI, [0.53;0.96]; p-value, 0.03) and lower risk of CKD hospitalization (hazard ratio, 0.6; 95 % CI, [0.37; 0.96]; p-value, 0.04) in the primary analysis. However, both associations were not significant in the sensitivity analysis. We did not observe significant association between use of glucagon-like peptide 1 receptor agonists (GLP-1RA), Thiazolidinediones (TZD), insulin and diagnosed CKD incidence, hospitalization or eGFR < 45 mL/min compared to use of SU as a second-line ADM.

Community-Driven Health Solutions on Chicago's South Side.

To reduce mortality for people experiencing cardiovascular health disparities, new innovations in health care must be implemented with strategic partnerships that involve trusted organizations and community members.

Developing High Performance Secure Multi-Party Computation Protocols in Healthcare: A Case Study of Patient Risk Stratification.

We demonstrate that secure multi-party computation (MPC) using garbled circuits is viable technology for solving clinical use cases that require cross-institution data exchange and collaboration. We describe two MPC protocols, based on Yao's garbled circuits and tested using large and realistically synthesized datasets. Linking records using private set intersection (PSI), we compute two metrics often used in patient risk stratification: high utilizer identification (PSI-HU) and comorbidity index calculation (PSI-CI). Cuckoo hashing enables our protocols to achieve extremely fast run times, with answers to clinically meaningful questions produced in minutes instead of hours. Also, our protocols are provably secure against any computationally bounded adversary in a semi-honest setting, the de-facto mode for cross-institution data analytics. Finally, these protocols eliminate the need for an implicitly trusted third-party "honest broker" to mediate the information linkage and exchange.

Evaluation of structured data from electronic health records to identify clinical classification criteria attributes for systemic lupus erythematosus.

OBJECTIVE: Our objective was to develop algorithms to identify lupus clinical classification criteria attributes using structured data found in the electronic health record (EHR) and determine whether they could be used to describe a cohort of people with lupus and discriminate them from a defined healthy control cohort. METHODS: We created gold standard lupus and healthy patient cohorts that were fully adjudicated for the American College of Rheumatology (ACR), Systemic Lupus International Collaborating Clinics (SLICC) and European League Against Rheumatism/ACR (EULAR/ACR) classification criteria and had matched EHR data. We implemented rule-based algorithms using structured data within the EHR system for each attribute of the three classification criteria. Individual criteria attribute and classification criteria algorithms as a whole were assessed over our combined cohorts and the overall performance of the algorithms was measured through sensitivity and specificity. RESULTS: Individual classification criteria attributes had a wide range of sensitivities, 7% (oral ulcers) to 97% (haematological disorders) and specificities, 56% (haematological disorders) to 98% (photosensitivity), but all could be identified in EHR data. In general, algorithms based on laboratory results performed better than those primarily based on diagnosis codes. All three classification criteria systems effectively distinguished members of our case and control cohorts, but the SLICC criteria-based algorithm had the highest overall performance (76% sensitivity, 99% specificity). CONCLUSIONS: It is possible to characterise disease manifestations in people with lupus using classification criteria-based algorithms that assess structured EHR data. These algorithms may reduce chart review burden and are a foundation for identifying subpopulations of patients with lupus based on disease presentation to support precision medicine applications.

Does coaching matter? Examining the impact of specific practice facilitation strategies on implementation of quality improvement interventions in the Healthy Hearts in the Heartland study.

BACKGROUND: Practice facilitation is a multicomponent implementation strategy used to improve the capacity for practices to address care quality and implementation gaps. We sought to assess whether practice facilitators use of coaching strategies aimed at improving self-sufficiency were associated with improved implementation of quality improvement (QI) interventions in the Healthy Hearts in the Heartland Study. METHODS: We mapped 27 practice facilitation activities to a framework that classifies practice facilitation strategies by the degree to which the practice develops its own process expertise (Doing Tasks, Project Management, Consulting, Teaching, and Coaching) and then used regression tree analysis to group practices by facilitation strategies experienced. Kruskal-Wallis tests were used to assess whether practice groups identified by regression tree analysis were associated with successful implementation of QI interventions and practice and study context variables. RESULTS: There was no association between number of strategies performed by practice facilitators and number of QI interventions implemented. Regression tree analysis identified 4 distinct practice groups based on the number of Project Management and Coaching strategies performed. The median number of interventions increased across the groups. Practices receiving > 4 project management and > 6 coaching activities implemented a median of 17 of 35 interventions. Groups did not differ significantly by practice size, association with a healthcare network, or practice type. Statistically significant differences in practice location, number and duration of facilitator visits, and early study termination emerged among the groups, compared to the overall practice population. CONCLUSIONS: Practices that engage in more coaching-based strategies with practice facilitators are more likely to implement more QI interventions, and practice receptivity to these strategies was not dependent on basic practice demographics.

Determining the feasibility of an index of the social determinants of health using data from public sources.

Examining the feasibility of developing an index measure for the social determinants of health using public data is needed. We examined these characteristics at the ZIP code in California and New York using public data extracted from the US Census, American Community Survey, the USDA Food Research Access Atlas, and the Dartmouth Atlas. We conducted a retrospective study from 2000 to 2017. The main outcome was a novel index measure representing six domains (economic stability, neighborhood and physical environment, education, community and social context, food access, and health care) and encompassing 13 items. The index measure at the ZIP code was created using principal component analysis, normalized to "0" worse and "1" better in California (ZIP codes n = 1,447 to 1,515) and New York (ZIP codes n = 1,211 to 1,298). We assessed the reliability and conducted a nonparametric comparison to the Robert Wood Johnson Foundation County Health Rankings, Area Deprivation Index, Social Deprivation Index, and GINI Index. These measures shared similarities and differences with the novel measure. Mapping of this novel measure showed regional variation. As a result, developing a universal social determinants of health measure is feasible and more research is needed to link it to health outcomes.

PCORnet® 2020: current state, accomplishments, and future directions.

OBJECTIVE: To describe PCORnet, a clinical research network developed for patient-centered outcomes research on a national scale. STUDY DESIGN AND SETTING: Descriptive study of the current state and future directions for PCORnet. We conducted cross-sectional analyses of the health systems and patient populations of the 9 Clinical Research Networks and 2 Health Plan Research Networks that are part of PCORnet. RESULTS: Within the Clinical Research Networks, electronic health data are currently collected from 337 hospitals, 169,695 physicians, 3,564 primary care practices, 338 emergency departments, and 1,024 community clinics. Patients can be recruited for prospective studies from any of these clinical sites. The Clinical Research Networks have accumulated data from 80 million patients with at least one visit from 2009 to 2018. The PCORnet Health Plan Research Network population of individuals with a valid enrollment segment from 2009 to 2019 exceeds 60 million individuals, who on average have 2.63 years of follow-up. CONCLUSION: PCORnet's infrastructure comprises clinical data from a diverse cohort of patients and has the capacity to rapidly access these patient populations for pragmatic clinical trials, epidemiological research, and patient-centered research on rare diseases.

Identifying Practice Facilitation Delays and Barriers in Primary Care Quality Improvement

OBJECTIVE: Practice facilitation is an effective approach to implementing quality improvement (QI) interventions in practice-based research networks (PBRNs). Regular facilitator-practice interactions are necessary for successful facilitation, and missed engagements may hinder the process of practice improvement. This study employs a mixed-methods approach to characterize the dynamics of practice facilitation and examine facilitation delays and barriers, as well as their association with the achievement of QI program goals in a PBRN initiative. METHODS: This study presents a secondary analysis of data from 226 primary care practices that participated in the Healthy Hearts in the Heartland (H3) initiative. We performed a time series analysis to identify delays in facilitation activities, and then qualitatively analyzed practice facilitators' notes (n = 4358) to uncover facilitation barriers. Finally, we assessed the relationship between delays, barriers, and QI intervention completion. RESULTS: While most facilitation activities occurred at regular, practice-specific tempos, nearly all practices experienced at least 1 delay. Practices with more delays had lower QI intervention completion rates. Practices with more delays were more likely to have encountered barriers such as lack of time and staff, lack of staff engagement, technical issues, and staff turnover. DISCUSSION AND CONCLUSION: This study is the first to quantify irregular intervals between facilitation activities and demonstrate their negative association with project completion. The analytic method can be applied to identify at-risk practices and to accelerate timely interventions in future studies. Our delay detection algorithm could inform the design of a decision support system that notifies facilitators which practices may benefit from timely attention and resources.

Defragmenting Heart Failure Care: Medical Records Integration.

Heart failure management requires intensive care coordination. Guideline-directed medical therapies have been shown to save lives but are practically challenging to implement because of the fragmented care that heart failure patients experience. Electronic health record adoption has transformed the collection and storage of clinical data, but accessing these data often remains prohibitively difficult. Current legislation aims to increase the interoperability of software systems so that providers and patients can easily access the clinical information they desire. Novel heart failure devices and technologies leverage patient-generated data to manage heart failure patients, whereas new data standards make it possible for this information to guide clinical decision-making.