Natural language processing to identify lupus nephritis phenotype in electronic health records.

BACKGROUND: Systemic lupus erythematosus (SLE) is a rare autoimmune disorder characterized by an unpredictable course of flares and remission with diverse manifestations. Lupus nephritis, one of the major disease manifestations of SLE for organ damage and mortality, is a key component of lupus classification criteria. Accurately identifying lupus nephritis in electronic health records (EHRs) would therefore benefit large cohort observational studies and clinical trials where characterization of the patient population is critical for recruitment, study design, and analysis. Lupus nephritis can be recognized through procedure codes and structured data, such as laboratory tests. However, other critical information documenting lupus nephritis, such as histologic reports from kidney biopsies and prior medical history narratives, require sophisticated text processing to mine information from pathology reports and clinical notes. In this study, we developed algorithms to identify lupus nephritis with and without natural language processing (NLP) using EHR data from the Northwestern Medicine Enterprise Data Warehouse (NMEDW). METHODS: We developed five algorithms: a rule-based algorithm using only structured data (baseline algorithm) and four algorithms using different NLP models. The first NLP model applied simple regular expression for keywords search combined with structured data. The other three NLP models were based on regularized logistic regression and used different sets of features including positive mention of concept unique identifiers (CUIs), number of appearances of CUIs, and a mixture of three components (i.e. a curated list of CUIs, regular expression concepts, structured data) respectively. The baseline algorithm and the best performing NLP algorithm were externally validated on a dataset from Vanderbilt University Medical Center (VUMC). RESULTS: Our best performing NLP model incorporated features from both structured data, regular expression concepts, and mapped concept unique identifiers (CUIs) and showed improved F measure in both the NMEDW (0.41 vs 0.79) and VUMC (0.52 vs 0.93) datasets compared to the baseline lupus nephritis algorithm. CONCLUSION: Our NLP MetaMap mixed model improved the F-measure greatly compared to the structured data only algorithm in both internal and external validation datasets. The NLP algorithms can serve as powerful tools to accurately identify lupus nephritis phenotype in EHR for clinical research and better targeted therapies.

Supermarket Proximity and Risk of Hypertension, Diabetes, and CKD: A Retrospective Cohort Study.

RATIONALE & OBJECTIVE: Living in environments with low access to food may increase the risk of chronic diseases. We investigated the association of household distance to the nearest supermarket (as a measure of food access) with the incidence of hypertension, diabetes, and chronic kidney disease (CKD) in a metropolitan area of the United States. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: 777,994 individuals without hypertension, diabetes, or CKD at baseline within the HealthLNK Data Repository, which contains electronic health records from 7 health care institutions in Chicago, Illinois. EXPOSURE: Zip code-level average distance between households and nearest supermarket. OUTCOME: Incidence of hypertension, diabetes, and CKD based on presence of ICD-9 code and/or blood pressure≥140/90mm Hg, hemoglobin A1c≥6.5%, and eGFR<60mL/min/1.73m2, respectively. ANALYTICAL APPROACH: Average distance to nearest supermarket was aggregated from street-level metrics for 56 Chicagoland zip codes. The cumulative incidence of hypertension, diabetes, and CKD from 2007-2012 was calculated for each zip code in patients free of these diseases in 2006. Spatial analysis of food access and disease incidence was performed using bivariate local indicator of spatial association (BiLISA) maps and bivariate local Moran I statistics. The relationship between supermarket access and outcomes was analyzed using logistic regression. RESULTS: Of 777,994 participants, 408,608 developed hypertension, 51,380 developed diabetes, and 56,365 developed CKD. There was significant spatial overlap between average distance to supermarket and incidence of hypertension and diabetes but not CKD. Zip codes with large average supermarket distances and high incidence of hypertension and diabetes were clustered in southern and western neighborhoods. Models adjusted only for neighborhood factors (zip code-level racial composition, access to vehicles, median income) revealed significant associations between zip code-level average distance to supermarket and chronic disease incidence. Relative to tertile 1 (shortest distance), ORs in tertiles 2 and 3, respectively, were 1.27 (95% CI, 1.23-1.30) and 1.38 (95% CI, 1.33-1.43) for diabetes, 1.03 (95% CI, 1.02-1.05) and 1.04 (95% CI, 1.02-1.06) for hypertension, and 1.18 (95% CI, 1.15-1.21) and 1.33 (95% CI, 1.29-1.37) for CKD. Models adjusted for demographic factors and health insurance showed significant and positive association with greater odds of incident diabetes (tertile 2: 1.29 [95% CI, 1.26-1.33]; tertile 3: 1.35 [95% CI, 1.31-1.39]) but lesser odds of hypertension (tertile 2: 0.95 [95% CI, 0.94-0.97]; tertile 3: 0.91 [95% CI, 0.89-0.92]) and CKD (tertile 2: 0.80 [95% CI, 0.78-0.82]; tertile 3: 0.73 [95% CI, 0.72-0.76]). After adjusting for both neighborhood and individual covariates, supermarket distance remained significantly associated with greater odds of diabetes and lesser odds of hypertension, but there was no significant association with CKD. LIMITATIONS: Unmeasured neighborhood and social confounding variables, zip code-level analysis, and limited individual-level information. CONCLUSIONS: There are significant disparities in supermarket proximity and incidence of hypertension, diabetes, and CKD in Chicago, Illinois. The relationship between supermarket access and chronic disease is largely explained by individual- and neighborhood-level factors.

Comparison of State-of-the-Art Neural Network Survival Models with the Pooled Cohort Equations for Cardiovascular Disease Risk Prediction.

BACKGROUND: The Pooled Cohort Equations (PCEs) are race- and sex-specific Cox proportional hazards (PH)-based models used for 10-year atherosclerotic cardiovascular disease (ASCVD) risk prediction with acceptable discrimination. In recent years, neural network models have gained increasing popularity with their success in image recognition and text classification. Various survival neural network models have been proposed by combining survival analysis and neural network architecture to take advantage of the strengths from both. However, the performance of these survival neural network models compared to each other and to PCEs in ASCVD prediction is unknown. METHODS: In this study, we used 6 cohorts from the Lifetime Risk Pooling Project (with 5 cohorts as training/internal validation and one cohort as external validation) and compared the performance of the PCEs in 10-year ASCVD risk prediction with an all two-way interactions Cox PH model (Cox PH-TWI) and three state-of-the-art neural network survival models including Nnet-survival, Deepsurv, and Cox-nnet. For all the models, we used the same 7 covariates as used in the PCEs. We fitted each of the aforementioned models in white females, white males, black females, and black males, respectively. We evaluated models' internal and external discrimination power and calibration. RESULTS: The training/internal validation sample comprised 23216 individuals. The average age at baseline was 57.8 years old (SD = 9.6); 16% developed ASCVD during average follow-up of 10.50 (SD = 3.02) years. Based on 10 × 10 cross-validation, the method that had the highest C-statistics was Deepsurv (0.7371) for white males, Deepsurv and Cox PH-TWI (0.7972) for white females, PCE (0.6981) for black males, and Deepsurv (0.7886) for black females. In the external validation dataset, Deepsurv (0.7032), Cox-nnet (0.7282), PCE (0.6811), and Deepsurv (0.7316) had the highest C-statistics for white male, white female, black male, and black female population, respectively. Calibration plots showed that in 10 × 10 validation, all models had good calibration in all race and sex groups. In external validation, all models overestimated the risk for 10-year ASCVD. CONCLUSIONS: We demonstrated the use of the state-of-the-art neural network survival models in ASCVD risk prediction. Neural network survival models had similar if not superior discrimination and calibration compared to PCEs.

Black Patients With Cirrhosis Have Higher Mortality and Lower Transplant Rates: Results From a Metropolitan Cohort Study.

BACKGROUND AND AIMS: Estimates of racial disparity in cirrhosis have been limited by lack of large-scale, longitudinal data, which track patients from diagnosis to death and/or transplant. APPROACH AND RESULTS: We analyzed a large, metropolitan, population-based electronic health record data set from seven large health systems linked to the state death registry and the national transplant database. Multivariate competing risk analyses, adjusted for sex, age, insurance status, Elixhauser score, etiology of cirrhosis, HCC, portal hypertensive complication, and Model for End-Stage Liver Disease-Sodium (MELD-Na), examined the relationship between race, transplant, and cause of death as defined by blinded death certificate review. During the study period, 11,277 patients met inclusion criteria, of whom 2,498 (22.2%) identified as Black. Compared to White patients, Black patients had similar age, sex, MELD-Na, and proportion of alcohol-associated liver disease, but higher comorbidity burden, lower rates of private insurance, and lower rates of portal hypertensive complications. Compared to White patients, Black patients had the highest rate all-cause mortality and non-liver-related death and were less likely to be listed or transplanted (P < 0.001 for all). In multivariate competing risk analysis, Black patients had a 26% increased hazard of liver-related death (subdistribution HR, 1.26; 95% CI, [1.15-1.38]; P < 0.001). CONCLUSIONS: Black patients with cirrhosis have discordant outcomes. Further research is needed to determine how to address these real disparities in the field of hepatology.

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.

Evaluating Natural Experiments that Impact the Diabetes Epidemic: an Introduction to the NEXT-D3 Network.

PURPOSE OF REVIEW: Diabetes is an ongoing public health issue in the USA, and, despite progress, recent reports suggest acute and chronic diabetes complications are increasing. RECENT FINDINGS: The Natural Experiments for Translation in Diabetes 3.0 (NEXT-D3) Network is a 5-year research collaboration involving six academic centers (Harvard University, Northwestern University, Oregon Health & Science University, Tulane University, University of California Los Angeles, and University of California San Francisco) and two funding agencies (Centers for Disease Control and Prevention and National Institutes of Health) to address the gaps leading to persisting diabetes burdens. The network builds on previously funded networks, expanding to include type 2 diabetes (T2D) prevention and an emphasis on health equity. NEXT-D3 researchers use rigorous natural experiment study designs to evaluate impacts of naturally occurring programs and policies, with a focus on diabetes-related outcomes. NEXT-D3 projects address whether and to what extent federal or state legislative policies and health plan innovations affect T2D risk and diabetes treatment and outcomes in the USA; real-world effects of increased access to health insurance under the Affordable Care Act; and the effectiveness of interventions that reduce barriers to medication access (e.g., decreased or eliminated cost sharing for cardiometabolic medications and new medications such as SGLT-2 inhibitors for Medicaid patients). Overarching goals include (1) expanding generalizable knowledge about policies and programs to manage or prevent T2D and educate decision-makers and organizations and (2) generating evidence to guide the development of health equity goals to reduce disparities in T2D-related risk factors, treatment, and complications.

Variability in comorbidites and health services use across homeless typologies: multicenter data linkage between healthcare and homeless systems.

BACKGROUND: Homelessness is associated with substantial morbidity. Data linkages between homeless and health systems are important to understand unique needs across homeless populations, identify homeless individuals not registered in homeless databases, quantify the impact of housing services on health-system use, and motivate health systems and payers to contribute to housing solutions. METHODS: We performed a cross-sectional survey including six health systems and two Homeless Management Information Systems (HMIS) in Cook County, Illinois. We performed privacy-preserving record linkage to identify homelessness through HMIS or ICD-10 codes captured in electronic medical records. We measured the prevalence of health conditions and health-services use across the following typologies: housing-service utilizers stratified by service provided (stable, stable plus unstable, unstable) and non-utilizers (i.e., homelessness identified through diagnosis codes-without receipt of housing services). RESULTS: Among 11,447 homeless recipients of healthcare, nearly 1 in 5 were identified by ICD10 code alone without recorded homeless services (n = 2177; 19%). Almost half received homeless services that did not include stable housing (n = 5444; 48%), followed by stable housing (n = 3017; 26%), then receipt of both stable and unstable services (n = 809; 7%). Setting stable housing recipients as the referent group, we found a stepwise increase in behavioral-health conditions from stable housing to those known as homeless solely by health systems. Compared to those in stable housing, prevalence rate ratios (PRR) for those without homeless services were as follows: depression (PRR = 2.2; 95% CI 1.9 to 2.5), anxiety (PRR = 2.5; 95% CI 2.1 to 3.0), schizophrenia (PRR = 3.3; 95% CI 2.7 to 4.0), and alcohol-use disorder (PRR = 4.4; 95% CI 3.6 to 5.3). Homeless individuals who had not received housing services relied on emergency departments for healthcare-nearly 3 of 4 visited at least one and many (24%) visited multiple. CONCLUSIONS: Differences in behavioral-health conditions and health-system use across homeless typologies highlight the particularly high burden among homeless who are disconnected from homeless services. Fragmented and high use of emergency departments for care should motivate health systems and payers to promote housing solutions, especially those that incorporate substance use and mental health treatment.

Liver-related mortality is similar among men and women with cirrhosis.

BACKGROUND & AIMS: Sex-based differences are known to significantly contribute to outcomes in patients with chronic liver diseases; however, the role of patient sex in cirrhosis is unclear. We aimed to study the relationship between patient sex and cirrhosis. METHODS: We analyzed a cohort of 20,045 patients with cirrhosis using a Chicago-wide electronic health record database that was linked with the United Network for Organ Sharing and cause of death data from the state death registry. Adjusted Cox survival analyses and competing risk analyses were performed to obtain subdistribution hazard ratios (HRs) for liver-related cause of death. RESULTS: Female and male patients had similar age, racial distribution, insurance status, and comorbidity status by Elixhauser score. Females had higher rates of cholestatic liver disease (17.1% vs. 6.2%, p <0.001) and non-alcoholic steatohepatitis (29.8% vs. 21.2%, p <0.001) than males. They were less likely to have portal hypertensive complications and had lower peak MELD-Na scores during follow-up. Female sex was associated with a decreased hazard of all-cause mortality (adjusted HR 0.85; 95% CI 0.80-0.90). This effect was attenuated when liver-related mortality was examined (subdistribution HR 0.93; 95% CI 0.87-1.00). No significant difference was noted for women who were 'ever-listed' in competing risk analyses for either all-cause mortality (subdistribution HR 1.09; 95% CI 0.88-1.35) or liver-related death (subdistribution HR 1.12; 95% CI 0.87-1.43), despite lower rates of listing (7.5% vs. 9.8%; p <0.001) and transplant (3.5% vs. 5.2%; p <0.001). CONCLUSIONS: In this longitudinal study of patients with cirrhosis, female sex was associated with a survival advantage likely driven by lower rates of non-liver-related death. Women were not at an increased risk of liver-related death despite lower rates of listing and transplantation. LAY SUMMARY: Patient sex is an important contributor in many chronic diseases, including cirrhosis. Prior studies have suggested that female sex is associated with worse outcomes. We analyzed a cohort of 20,045 patients with cirrhosis using a Chicago-wide electronic health record database. Using multivariate competing risk analyses, we found that female sex in cirrhosis is actually associated with a lower risk of all-cause mortality and has no association with liver-related mortality. Our findings are novel because we show that women with cirrhosis have a similar risk of liver-related death as their male counterparts, despite lower rates of listing and transplantation.

Effects of 2 Forms of Practice Facilitation on Cardiovascular Prevention in Primary Care: A Practice-randomized, Comparative Effectiveness Trial.

BACKGROUND: Effective quality improvement (QI) strategies are needed for small practices. OBJECTIVE: The objective of this study was to compare practice facilitation implementing point-of-care (POC) QI strategies alone versus facilitation implementing point-of-care plus population management (POC+PM) strategies on preventive cardiovascular care. DESIGN: Two arm, practice-randomized, comparative effectiveness study. PARTICIPANTS: Small and mid-sized primary care practices. INTERVENTIONS: Practices worked with facilitators on QI for 12 months to implement POC or POC+PM strategies. MEASURES: Proportion of eligible patients in a practice meeting "ABCS" measures: (Aspirin) Aspirin/antiplatelet therapy for ischemic vascular disease, (Blood pressure) Controlling High Blood Pressure, (Cholesterol) Statin Therapy for the Prevention and Treatment of Cardiovascular Disease, and (Smoking) Tobacco Use: Screening and Cessation Intervention, and the Change Process Capability Questionnaire. Measurements were performed at baseline, 12, and 18 months. RESULTS: A total of 226 practices were randomized, 179 contributed follow-up data. The mean proportion of patients meeting each performance measure was greater at 12 months compared with baseline: Aspirin 0.04 (95% confidence interval: 0.02-0.06), Blood pressure 0.04 (0.02-0.06), Cholesterol 0.05 (0.03-0.07), Smoking 0.05 (0.02-0.07); P<0.001 for each. Improvements were sustained at 18 months. At 12 months, baseline-adjusted difference-in-differences in proportions for the POC+PM arm versus POC was: Aspirin 0.02 (-0.02 to 0.05), Blood pressure -0.01 (-0.04 to 0.03), Cholesterol 0.03 (0.00-0.07), and Smoking 0.02 (-0.02 to 0.06); P>0.05 for all. Change Process Capability Questionnaire improved slightly, mean change 0.30 (0.09-0.51) but did not significantly differ across arms. CONCLUSION: Facilitator-led QI promoting population management approaches plus POC improvement strategies was not clearly superior to POC strategies alone.

Implementation of Community-Based Resource Referrals for Cardiovascular Disease Self-Management.

PURPOSE: Describe primary care practices' implementation of CommunityRx-H3, a community resource referral intervention that utilized practice facilitators to support cardiovascular disease (CVD) prevention quality improvement. METHODS: Qualitative focus groups were conducted with practice facilitators to elicit perceptions of practices' experiences with CommunityRx-H3, practice-level factors affecting, and practice facilitator strategies to promote implementation. Qualitative data were analyzed using directed content analysis. The Consolidated Framework for Implementation Research was applied deductively to organize and interpret findings. RESULTS: Fourteen of all 19 practice facilitators participated. Practice facilitators perceived that staff attitudes about connecting patients to community resources for CVD were largely positive. Practices were already using a range of non-systematic strategies to refer to community resources. Practice-level factors that facilitated CommunityRx-H3 implementation included clinician "champions," engaged practice managers, and a practice culture that valued community resources. Implementation barriers included a practice's unwillingness to integrate the intervention into existing workflows, limited staff capacity to complete the resource inventory, and unavailability or cost of materials needed to print the resource referral list ("HealtheRx-H3"). Practice facilitator strategies to promote implementation included supporting ongoing customization of the HealtheRx-H3 and material support. Practice facilitators felt implementation would be improved by integration of CommunityRx-H3 with electronic medical record workflows and alternative methods for engaging practices in the implementation process. CONCLUSIONS: Practice facilitators are increasingly being utilized by primary care practices to support quality improvement interventions and, as shown here, can also play an important role in implementation science. This study yields insights to improve implementation of community resource referral solutions to support primary care CVD prevention efforts.

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.

Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements.

BACKGROUND: The development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality. METHODS: Our objective was to develop and validate a data driven multivariable clinical predictive model for early detection of AKI among a large cohort of adult critical care patients. We utilized data form the Medical Information Mart for Intensive Care III (MIMIC-III) for all patients who had a creatinine measured for 3 days following ICU admission and excluded patients with pre-existing condition of Chronic Kidney Disease and Acute Kidney Injury on admission. Data extracted included patient age, gender, ethnicity, creatinine, other vital signs and lab values during the first day of ICU admission, whether the patient was mechanically ventilated during the first day of ICU admission, and the hourly rate of urine output during the first day of ICU admission. RESULTS: Utilizing the demographics, the clinical data and the laboratory test measurements from Day 1 of ICU admission, we accurately predicted max serum creatinine level during Day 2 and Day 3 with a root mean square error of 0.224 mg/dL. We demonstrated that using machine learning models (multivariate logistic regression, random forest and artificial neural networks) with demographics and physiologic features can predict AKI onset as defined by the current clinical guideline with a competitive AUC (mean AUC 0.783 by our all-feature, logistic-regression model), while previous models aimed at more specific patient cohorts. CONCLUSIONS: Experimental results suggest that our model has the potential to assist clinicians in identifying patients at greater risk of new onset of AKI in critical care setting. Prospective trials with independent model training and external validation cohorts are needed to further evaluate the clinical utility of this approach and potentially instituting interventions to decrease the likelihood of developing AKI.

Validity of Cardiovascular Data From Electronic Sources: The Multi-Ethnic Study of Atherosclerosis and HealthLNK.

BACKGROUND: Understanding the validity of data from electronic data research networks is critical to national research initiatives and learning healthcare systems for cardiovascular care. Our goal was to evaluate the degree of agreement of electronic data research networks in comparison with data collected by standardized research approaches in a cohort study. METHODS: We linked individual-level data from MESA (Multi-Ethnic Study of Atherosclerosis), a community-based cohort, with HealthLNK, a 2006 to 2012 database of electronic health records from 6 Chicago health systems. To evaluate the correlation and agreement of blood pressure in HealthLNK in comparison with in-person MESA examinations, and body mass index in HealthLNK in comparison with MESA, we used Pearson correlation coefficients and Bland-Altman plots. Using diagnoses in MESA as the criterion standard, we calculated the performance of HealthLNK for hypertension, obesity, and diabetes mellitus diagnosis by using International Classification of Diseases, Ninth Revision codes and clinical data. We also identified potential myocardial infarctions, strokes, and heart failure events in HealthLNK and compared them with adjudicated events in MESA. RESULTS: Of the 1164 MESA participants enrolled at the Chicago Field Center, 802 (68.9%) participants had data in HealthLNK. The correlation was low for systolic blood pressure (0.39; P<0.0001). In comparison with MESA, HealthLNK overestimated systolic blood pressure by 6.5 mm Hg (95% confidence interval, 4.2-7.8). There was a high correlation between body mass index in MESA and HealthLNK (0.94; P<0.0001). HealthLNK underestimated body mass index by 0.3 kg/m2 (95% confidence interval, -0.4 to -0.1). With the use of International Classification of Diseases, Ninth Revision codes and clinical data, the sensitivity and specificity of HealthLNK queries for hypertension were 82.4% and 59.4%, for obesity were 73.0% and 89.8%, and for diabetes mellitus were 79.8% and 93.3%. In comparison with adjudicated cardiovascular events in MESA, the concordance rates for myocardial infarction, stroke, and heart failure were, respectively, 41.7% (5/12), 61.5% (8/13), and 62.5% (10/16). CONCLUSIONS: These findings illustrate the limitations and strengths of electronic data repositories in comparison with information collected by traditional standardized epidemiological approaches for the ascertainment of cardiovascular risk factors and events.

Impact of Practice Facilitation in Primary Care on Chronic Disease Care Processes and Outcomes: a Systematic Review.

BACKGROUND: More than 100 million individuals in the USA have been diagnosed with a chronic disease, yet chronic disease care has remained fragmented and of inconsistent quality. Improving chronic disease management has been challenging for primary care and internal medicine practitioners. Practice facilitation provides a comprehensive approach to chronic disease care. The objective is to evaluate the impact of practice facilitation on chronic disease outcomes in the primary care setting. METHODS: This systematic review examined North American studies from PubMed, EMBASE, and Web of Science (database inception to August 2017). Investigators independently extracted and assessed the quality of the data on chronic disease process and clinical outcome measures. Studies implemented practice facilitation and reported quantifiable care processes and patient outcomes for chronic disease. Each study and their evidence were assessed for risk of bias and quality according to the Cochrane Collaboration and the Grade Collaboration tool. RESULTS: This systematic review included 25 studies: 12 randomized control trials and 13 prospective cohort studies. Across all studies, practices and their clinicians were aware of the implementation of practice facilitation. Improvements were observed in most studies for chronic diseases including asthma, cancer (breast, cervical, and colorectal), cardiovascular disease (cerebrovascular disease, coronary artery disease, dyslipidemia, hypertension, myocardial infarction, and peripheral vascular disease), and type 2 diabetes. Mixed results were observed for chronic kidney disease and chronic illness care. DISCUSSION: Overall, the results suggest that practice facilitation may improve chronic disease care measures. Across all studies, practices were aware of practice facilitation. These findings lend support for the potential expansion of practice facilitation in primary care. Future work will need to investigate potential opportunities for practice facilitation to improve chronic disease outcomes in other health care settings (e.g., specialty and multi-specialty practices) with standardized measures.

Introductory Overview of the Natural Experiments for Translation in Diabetes 2.0 (NEXT-D2) Network: Examining the Impact of US Health Policies and Practices to Prevent Diabetes and Its Complications.

PURPOSE OF REVIEW: Diabetes incidence is rising among vulnerable population subgroups including minorities and individuals with limited education. Many diabetes-related programs and public policies are unevaluated while others are analyzed with research designs highly susceptible to bias which can result in flawed conclusions. The Natural Experiments for Translation in Diabetes 2.0 (NEXT-D2) Network includes eight research centers and three funding agencies using rigorous methods to evaluate natural experiments in health policy and program delivery. RECENT FINDINGS: NEXT-D2 research studies use quasi-experimental methods to assess three major areas as they relate to diabetes: health insurance expansion; healthcare financing and payment models; and innovations in care coordination. The studies will report on preventive processes, achievement of diabetes care goals, and incidence of complications. Some studies assess healthcare utilization while others focus on patient-reported outcomes. NEXT-D2 examines the effect of public and private policies on diabetes care and prevention at a critical time, given ongoing and rapid shifts in the US health policy landscape.

Engaging Primary Care Practices in Studies of Improvement: Did You Budget Enough for Practice Recruitment?

PURPOSE: The methods and costs to enroll small primary care practices in large, regional quality improvement initiatives are unknown. We describe the recruitment approach, cost, and resources required to recruit and enroll 500 practices in the Northwest and Midwest regional cooperatives participating in the Agency for Healthcare Research and Quality (AHRQ)-funded initiative, EvidenceNOW: Advancing Heart Health in Primary Care. METHODS: The project management team of each cooperative tracked data on recruitment methods used for identifying and connecting with practices. We developed a cost-of-recruitment template and used it to record personnel time and associated costs of travel and communication materials. RESULTS: A total of 3,669 practices were contacted during the 14- to 18-month recruitment period, resulting in 484 enrolled practices across the 6 states served by the 2 cooperatives. The average number of interactions per enrolled practice was 7, with a total of 29,100 hours and a total cost of $2.675 million, or $5,529 per enrolled practice. Prior partnerships predicted recruiting almost 1 in 3 of these practices as contrasted to 1 in 20 practices without a previous relationship or warm hand-off. CONCLUSIONS: Recruitment of practices for large-scale practice quality improvement transformation initiatives is difficult and costly. The cost of recruiting practices without existing partnerships is expensive, costing 7 times more than reaching out to familiar practices. Investigators initiating and studying practice quality improvement initiatives should budget adequate funds to support high-touch recruitment strategies, including building trusted relationships over a long time frame, for a year or more.

Learning bundled care opportunities from electronic medical records.

OBJECTIVE: The traditional fee-for-service approach to healthcare can lead to the management of a patient's conditions in a siloed manner, inducing various negative consequences. It has been recognized that a bundled approach to healthcare - one that manages a collection of health conditions together - may enable greater efficacy and cost savings. However, it is not always evident which sets of conditions should be managed in a bundled manner. In this study, we investigate if a data-driven approach can automatically learn potential bundles. METHODS: We designed a framework to infer health condition collections (HCCs) based on the similarity of their clinical workflows, according to electronic medical record (EMR) utilization. We evaluated the framework with data from over 16,500 inpatient stays from Northwestern Memorial Hospital in Chicago, Illinois. The plausibility of the inferred HCCs for bundled care was assessed through an online survey of a panel of five experts, whose responses were analyzed via an analysis of variance (ANOVA) at a 95% confidence level. We further assessed the face validity of the HCCs using evidence in the published literature. RESULTS: The framework inferred four HCCs, indicative of (1) fetal abnormalities, (2) late pregnancies, (3) prostate problems, and (4) chronic diseases, with congestive heart failure featuring prominently. Each HCC was substantiated with evidence in the literature and was deemed plausible for bundled care by the experts at a statistically significant level. CONCLUSIONS: The findings suggest that an automated EMR data-driven framework conducted can provide a basis for discovering bundled care opportunities. Still, translating such findings into actual care management will require further refinement, implementation, and evaluation.

A computable phenotype for asthma case identification in adult and pediatric patients: External validation in the Chicago Area Patient-Outcomes Research Network (CAPriCORN).

Objective: Comprehensive, rapid, and accurate identification of patients with asthma for clinical care and engagement in research efforts is needed. The original development and validation of a computable phenotype for asthma case identification occurred at a single institution in Chicago and demonstrated excellent test characteristics. However, its application in a diverse payer mix, across different health systems and multiple electronic health record vendors, and in both children and adults was not examined. The objective of this study is to externally validate the computable phenotype across diverse Chicago institutions to accurately identify pediatric and adult patients with asthma. Methods: A cohort of 900 asthma and control patients was identified from the electronic health record between January 1, 2012 and November 30, 2014. Two physicians at each site independently reviewed the patient chart to annotate cases. Results: The inter-observer reliability between the physician reviewers had a κ-coefficient of 0.95 (95% CI 0.93-0.97). The accuracy, sensitivity, specificity, negative predictive value, and positive predictive value of the computable phenotype were all above 94% in the full cohort. Conclusions: The excellent positive and negative predictive values in this multi-center external validation study establish a useful tool to identify asthma cases in in the electronic health record for research and care. This computable phenotype could be used in large-scale comparative-effectiveness trials.