Improving chronic kidney disease detection and treatment in the United States: the chronic kidney disease cascade of care (C3) study protocol.

BACKGROUND: There are major gaps in the implementation of guideline-concordant care for persons with chronic kidney disease (CKD). The CKD Cascade of Care (C3) initiative seeks to improve CKD care by improving detection and treatment of CKD in primary care. METHODS: C3 is a multi-modal initiative deployed in three major academic medical centers within the Department of Veterans Affairs (VA) Health Care System: San Francisco VA, San Diego VA, and Houston VA. The main objective of the first phase of C3 described in this protocol is to establish the infrastructure for universal CKD detection among primary care patients at high-risk for CKD with a triple-marker screen comprising cystatin C, creatinine, and albuminuria. Across the three sites, a comprehensive educational intervention and the integration of primary care-based clinical champions will be employed with the goal of improving CKD detection and treatment. The San Francisco VA will also implement a practice-facilitation intervention leveraging telehealth and health informatics tools and capabilities for enhanced CKD detection. Parallel formative evaluation across the three sites will assess the feasibility and acceptability of integrating cystatin C as part of routine CKD detection in primary care practice. The effectiveness of the interventions will be assessed using a pre-post observational design for change in the proportion of patients tested annually for CKD. Secondary outcomes will assess change in the initiation of cardio-kidney protective therapies and in nephrology referrals of high-risk patients. DISCUSSION: The first phase of C3 is a multi-facility multi-modal initiative that aims to improve CKD care by implementing a triple-marker screen for enhanced CKD detection in primary care.

Anticipating VA/non-VA care coordination demand for Veterans at high risk for hospitalization.

ABSTRACT: U.S. Veterans Affairs (VA) patients' multi-system use can create challenges for VA clinicians who are responsible for coordinating Veterans' use of non-VA care, including VA-purchased care ("Community Care") and Medicare.To examine the relationship between drive distance and time-key eligibility criteria for Community Care-and VA reliance (proportion of care received in VA versus Medicare and Community Care) among Veterans at high risk for hospitalization. We used prepolicy data to anticipate the impact of the 2014 Choice Act and 2018 Maintaining Internal Systems and Strengthening Integrated Outside Networks Act (MISSION Act), which expanded access to Community Care.Cross-sectional analysis using fractional logistic regressions to examine the relationship between a Veteran's reliance on VA for outpatient primary, mental health, and other specialty care and their drive distance/time to a VA facility.Thirteen thousand seven hundred three Veterans over the age of 65 years enrolled in VA and fee-for-service Medicare in federal fiscal year 2014 who were in the top 10th percentile for hospitalization risk.Key explanatory variables were patients' drive distance to VA > 40 miles (Choice Act criteria) and drive time to VA ≥ 30 minutes for primary and mental health care and ≥60 minutes for specialty care (MISSION Act criteria).Veterans at high risk for hospitalization with drive distance eligibility had increased odds of an outpatient specialty care visit taking place in VA when compared to Veterans who did not meet Choice Act eligibility criteria (odds ratio = 1.10, 95% confidence interval 1.05-1.15). However, drive time eligibility (MISSION Act criteria) was associated with significantly lower odds of an outpatient specialty care visit taking place in VA (odds ratio = 0.69, 95% confidence interval 0.67, 0.71). Neither drive distance nor drive time were associated with reliance for outpatient primary care or mental health care.VA patients who are at high risk for hospitalization may continue to rely on VA for outpatient primary care and mental health care despite access to outside services, but may increase use of outpatient specialty care in the community in the MISSION era, increasing demand for multi-system care coordination.

Outpatient care fragmentation in Veterans Affairs patients at high-risk for hospitalization.

OBJECTIVE: To examine outpatient care fragmentation and its association with future hospitalization among patients at high risk for hospitalization. DATA SOURCES: Veterans Affairs (VA) and Medicare data. STUDY DESIGN: We conducted a longitudinal study, using logistic regression to examine how outpatient care fragmentation in FY14 (as measured by number of unique providers, Breslau's Usual Provider of Care (UPC), Bice-Boxerman's Continuity of Care Index (COCI), and Modified Modified Continuity Index (MMCI)) was associated with all-cause hospitalizations and hospitalizations related to ambulatory care sensitive conditions (ACSC) in FY15. We also examined how fragmentation varied by patient's age, gender, race, ethnicity, marital status, rural status, history of homelessness, number of chronic conditions, Medicare utilization, and mental health care utilization. DATA EXTRACTION METHODS: We extracted data for 130,704 VA patients ≥65 years old with a hospitalization risk ≥90th percentile and ≥ four outpatient visits in the baseline year. PRINCIPAL FINDINGS: The mean (SD) of FY14 outpatient visits was 13.2 (8.6). Fragmented care (more providers, less care with a usual provider, more dispersed care based on COCI) was more common among patients with more chronic conditions and those receiving mental health care. In adjusted models, most fragmentation measures were not associated with all-cause hospitalization, and patients with low levels of fragmentation (more concentrated care based on UPC, COCI, and MMCI) had a higher likelihood of an ACSC-related hospitalization (AOR, 95% CI = 1.21 (1.09-1.35), 1.27 (1.14-1.42), and 1.28 (1.18-1.40), respectively). CONCLUSIONS: Contrary to expectations, outpatient care fragmentation was not associated with elevated all-cause hospitalization rates among VA patients in the top 10th percentile for risk of admission; in fact, fragmented care was linked to lower rates of hospitalization for ACSCs. In integrated settings such as the VA, multiple providers, and dispersed care might offer access to timely or specialized care that offsets risks of fragmentation, particularly for conditions that are sensitive to ambulatory care.

Structural Patterns in Chronic Disease Clinical Practice Guidelines Formalized for Clinical Decision Support.

Making recommendations from clinical practice guidelines (CPGs) computable for clinical decision support (CDS) has typically been a laborious and costly process. Identifying domain-specific regularities helps clinicians and knowledge engineers conceptualize, extract, and encode evidence-based recommendations. Based on our work to provide complex CDS in the management of multiple chronic diseases, we propose nine chronic disease CPG structural patterns, discuss considerations in representing the necessary knowledge, and illustrate them with the solutions that our CDS system provides.

Nephrology Referral Based on Laboratory Values, Kidney Failure Risk, or Both: A Study Using Veterans Affairs Health System Data.

RATIONALE & OBJECTIVE: Current guidelines for nephrology referral are based on laboratory criteria. We sought to evaluate whether nephrology referral patterns reflect current clinical practice guidelines and to estimate the change in referral volume if they were based on the estimated risk of kidney failure. STUDY DESIGN: Observational cohort. SETTING & PARTICIPANTS: Retrospective study of 399,644 veterans with chronic kidney disease (October 1, 2015 through September 30, 2016). EXPOSURE: Laboratory referral criteria based on Veterans Affairs/Department of Defense guidelines, categories of predicted risk for kidney failure using the Kidney Failure Risk Equation, and the combination of laboratory referral criteria and predicted risk. OUTCOME: Number of patients identified for referral. ANALYTICAL APPROACH: We evaluated the number of patients who were referred and their predicted 2-year risk for kidney failure. For each exposure, we estimated the number of patients who would be identified for referral. RESULTS: There were 66,276 patients who met laboratory indications for referral. Among these patients, 11,752 (17.7%) were referred to nephrology in the following year. The median 2-year predicted risk of kidney failure was 1.5% (interquartile range, 0.3%-4.7%) among all patients meeting the laboratory referral criteria. If referrals were restricted to patients with a predicted risk of ≥1% in addition to laboratory indications, the potential referral volume would be reduced from 66,276 to 38,229 patients. If referrals were based on predicted risk alone, a 2-year risk threshold of 1% or higher would identify a similar number of patients (72,948) as laboratory-based criteria with median predicted risk of 2.3% (interquartile range, 1.4%-4.6%). LIMITATIONS: Missing proteinuria measurements. CONCLUSIONS: The current laboratory-based guidelines for nephrology referral identify patients who are, on average, at low risk for progression, most of whom are not referred. As an alternative, referral based on a 2-year kidney failure risk exceeding 1% would identify a similar number of patients but with a higher median risk of kidney failure.

Renin-angiotensin-aldosterone system inhibitors and SARS-CoV-2 infection: an analysis from the veteran's affairs healthcare system.

BACKGROUND: Angiotensin-converting enzyme inhibitors (ACEI) and angiotensin receptor blockers (ARB) are known to impact the functional receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The association between chronic therapy with these medications and infection risk remains unclear. OBJECTIVES: The objective was to determine the association between prior ACEI or ARB therapy and SARS-CoV-2 infection among patients with hypertension in the U.S. Veteran's Affairs health system. METHODS: We compared the odds of SARS-CoV-2 infection among three groups: patients treated with ACEI, treated with ARB, or treated with alternate first-line anti-hypertensives without ACEI/ARB. We excluded patients with alternate indications for ACEI or ARB therapy. We performed an augmented inverse propensity weighted analysis with adjustment for demographics, region, comorbidities, vitals, and laboratory values. RESULTS: Among 1,724,723 patients with treated hypertension, 659,180 were treated with ACEI, 310,651 with ARB, and 754,892 with neither. Before weighting, patients treated with ACEI or ARB were more likely to be diabetic and use more anti-hypertensives. There were 13,278 SARS-CoV-2 infections (0.8%) between February 12, 2020 and August 19, 2020. Patients treated with ACEI had lower odds of SARS-CoV-2 infection (odds ratio [OR] 0.93; 95% CI: 0.89-0.97) while those treated with ARB had similar odds (OR 1.02; 95% CI: 0.96-1.07) compared with patients treated with alternate first-line anti-hypertensives without ACEI/ARB. In falsification analyses, patients on ACEI did not have a difference in their odds of unrelated outcomes. CONCLUSIONS: Our results suggest the safety of continuing ACEI and ARB therapy. The association between ACEI therapy and lower odds of SARS-CoV-2 infection requires further investigation.

Proportion of SARS-CoV-2 positive tests and vaccination in Veterans Affairs Community Living Centers.

BACKGROUND/OBJECTIVES: COVID-19 has caused significant morbidity and mortality in nursing homes. Vaccination against SARS-COV-2 holds promise for reduction in COVID-19. This operational analysis describes the proportion of SARS-COV-2 positive tests before, during, and after vaccination. DESIGN: Retrospective longitudinal cohort analysis from October 1, 2020 until February 14, 2021. SETTING: A total of 130 Department of Veterans Affairs (VA) Community Living Centers (CLC), analogous to nursing homes. INTERVENTION: Vaccination for SARS-CoV-2. MEASUREMENTS: The primary measure is the proportion of SARS-CoV-2 positive tests among CLC residents. In a pooled analysis of weekly testing and vaccine data, the proportion of positive tests was compared for the unvaccinated, first dose, and second dose. For each CLC, we identified the week in which 50% of CLC residents were vaccinated (index week). The analysis aligned the index week for CLCs and examined the proportion of SARS-CoV-2 positive tests at the CLC level before and after. As a reference, we plotted the proportion of positive tests in nursing homes in the same county as the CLC using publicly reported data. RESULTS: Within the pooled VA CLCs, the first SARS-CoV-2 vaccine dose was delivered to 50% of CLC residents within 1 week of availability and second dose within 5 weeks. Relative to the index week, the risk ratio of SARS-CoV-2 positive tests in the vaccinated relative to unvaccinated was significantly lower in Week 4 (relative risk 0.37, 95% confidence interval 0.20-0.68). Throughout the study period, the proportion of SARS-CoV-2 positive tests in community nursing homes was higher compared to VA CLC and also declined after vaccine availability. CONCLUSION: The proportion of SARS-CoV-2 positive tests significantly declined in VA CLCs 4 weeks after vaccine delivery and continued to decline in vaccinated and unvaccinated residents. The results describe the importance of SARS-CoV-2 surveillance and vaccination in VA nursing home residents.

Laboratory-wide association study of survival with prostate cancer.

BACKGROUND: Estimates of overall patient health are essential to inform treatment decisions for patients diagnosed with cancer. The authors applied XWAS methods, herein referred to as "laboratory-wide association study (LWAS)", to evaluate associations between routinely collected laboratory tests and survival in veterans with prostate cancer. METHODS: The authors identified 133,878 patients who were diagnosed with prostate cancer between 2000 and 2013 in the Veterans Health Administration using any laboratory tests collected within 6 months of diagnosis (3,345,083 results). Using the LWAS framework, the false-discovery rate was used to test the association between multiple laboratory tests and survival, and these results were validated using training, testing, and validation cohorts. RESULTS: A total of 31 laboratory tests associated with survival met stringent LWAS criteria. LWAS confirmed markers of prostate cancer biology (prostate-specific antigen: hazard ratio [HR], 1.07 [95% confidence interval (95% CI), 1.06-1.08]; and alkaline phosphatase: HR, 1.22 [95% CI, 1.20-1.24]) as well laboratory tests of general health (eg, serum albumin: HR, 0.78 [95% CI, 0.76-0.80]; and creatinine: HR, 1.05 [95% CI, 1.03-1.07]) and inflammation (leukocyte count: HR, 1.23 [95% CI, 1.98-1.26]; and erythrocyte sedimentation rate: HR, 1.33 [95% CI, 1.09-1.61]). In addition, the authors derived and validated separate models for patients with localized and advanced disease, identifying 28 laboratory markers and 15 laboratory markers, respectively, in each cohort. CONCLUSIONS: The authors identified routinely collected laboratory data associated with survival for patients with prostate cancer using LWAS methodologies, including markers of prostate cancer biology, overall health, and inflammation. Broadening consideration of determinants of survival beyond those related to cancer itself could help to inform the design of clinical trials and aid in shared decision making. LAY SUMMARY: This article examined routine laboratory tests associated with survival among veterans with prostate cancer. Using laboratory-wide association studies, the authors identified 31 laboratory tests associated with survival that can be used to inform the design of clinical trials and aid patients in shared decision making.

Guided self-management targeting anxiety and activity participation in older Veterans.

OBJECTIVES: This study examined the acceptance, feasibility, and preliminary effects of a guided self-management intervention using video delivery and a telephone coach on anxiety and activity engagement. METHOD: Ten Veterans aged 60 years or older with anxiety disorders determined by Structured Clinical Interview for Diagnostic and Statistical Manual 5th edition (SCID-5) at baseline visit participated in this non-randomized study examining a 4-week guided self-management intervention for anxiety. Feasibility was examined using participation engagement with the intervention. Measures of anxiety (Geriatric Anxiety Scale, PROMIS Anxiety Scale, Anxiety Control Questionnaire), depression (Patient Health Questionnaire 9-item), and activity participation (modified Activity Card Sort) administered at baseline and final (week 8) visit provided estimates of preliminary intervention effects. The Geriatric Anxiety Scale also was administered by phone at week 4. Participants completed a semi-structured qualitative interview at the final visit, which provided information about the acceptability, benefits of intervention, and barriers to engagement. RESULTS: All participants (N = 10) reported that the intervention somewhat or completely met their expectations, demonstrating intervention acceptability. Intervention completers (n = 9) experienced reduced anxiety over the first 4 weeks, alongside significant improvements in anxiety control and personalized activity goals across 8 weeks. However, anxiety symptoms tended to return to baseline at follow-up. Participants identified the relaxation videos and promotion of a daily relaxation routine as the most helpful intervention components. CONCLUSIONS: Findings indicate that the intervention may improve activity participation and reduce anxiety. Thus, guided self-management interventions show promise for reducing distress and maintaining engagement later in life.

Cataract Surgery Complexity and Surgical Complication Rates Among Medicare Beneficiaries With and Without Dementia.

PURPOSE: To evaluate cataract surgery complexity and complications among US Medicare beneficiaries with and without dementia. DESIGN: Retrospective claims-based cohort study. PARTICIPANTS: A 20% representative sample of Medicare beneficiaries, 2006-2015. METHODS: Dementia was identified from diagnosis codes on or prior to each beneficiary's first-eye cataract surgery. For each surgery, we identified setting, routine vs complex coding, anesthesia provider type, duration, and any postoperative hospitalization. We evaluated 30- and 90-day complication rates-return to operating room, endophthalmitis, suprachoroidal hemorrhage, retinal detachment, retinal tear, macular edema, glaucoma, or choroidal detachment-and used adjusted regression models to evaluate likelihood of surgical characteristics and complications. Complication analyses were stratified by second-eye cataract surgery within 90 days postoperatively. RESULTS: We identified 457,128 beneficiaries undergoing first-eye cataract surgery, 23,332 (5.1%) with dementia. None of the evaluated surgical complications were more likely in dementia-diagnosed beneficiaries. There was also no difference in likelihood of nonambulatory surgery center setting, anesthesiologist provider, or postoperative hospitalization. Dementia-diagnosed beneficiaries were more likely to have surgeries coded as complex (15.6% of cases vs 8.8%, P < .0001), and surgeries exceeding 30 minutes (OR = 1.21, 95% CI = 1.17-1.25). CONCLUSIONS: Among US Medicare beneficiaries undergoing cataract surgery, those with dementia are more likely to have "complex" surgery" lasting more than 30 minutes. However, they do not have greater likelihood of surgical complications, higher-acuity setting, advanced anesthesia care, or postoperative hospitalization. This may be influenced by case selection and may suggest missed opportunities to improve vision. Future research is needed to identify dementia patients likely to benefit from cataract surgery.

OrderRex clinical user testing: a randomized trial of recommender system decision support on simulated cases.

OBJECTIVE: To assess usability and usefulness of a machine learning-based order recommender system applied to simulated clinical cases. MATERIALS AND METHODS: 43 physicians entered orders for 5 simulated clinical cases using a clinical order entry interface with or without access to a previously developed automated order recommender system. Cases were randomly allocated to the recommender system in a 3:2 ratio. A panel of clinicians scored whether the orders placed were clinically appropriate. Our primary outcome included the difference in clinical appropriateness scores. Secondary outcomes included total number of orders, case time, and survey responses. RESULTS: Clinical appropriateness scores per order were comparable for cases randomized to the order recommender system (mean difference -0.11 order per score, 95% CI: [-0.41, 0.20]). Physicians using the recommender placed more orders (median 16 vs 15 orders, incidence rate ratio 1.09, 95%CI: [1.01-1.17]). Case times were comparable with the recommender system. Order suggestions generated from the recommender system were more likely to match physician needs than standard manual search options. Physicians used recommender suggestions in 98% of available cases. Approximately 95% of participants agreed the system would be useful for their workflows. DISCUSSION: User testing with a simulated electronic medical record interface can assess the value of machine learning and clinical decision support tools for clinician usability and acceptance before live deployments. CONCLUSIONS: Clinicians can use and accept machine learned clinical order recommendations integrated into an electronic order entry interface in a simulated setting. The clinical appropriateness of orders entered was comparable even when supported by automated recommendations.

Life expectancy estimates for patients diagnosed with prostate cancer in the Veterans Health Administration.

PURPOSE: Accurate life expectancy estimates are required to inform prostate cancer treatment decisions. However, few models are specific to the population served or easily implemented in a clinical setting. We sought to create life expectancy estimates specific to Veterans diagnosed with prostate cancer. MATERIALS AND METHODS: Using national Veterans Health Administration electronic health records, we identified Veterans diagnosed with prostate cancer between 2000 and 2015. We abstracted demographics, comorbidities, oncologic staging, and treatment information. We fit Cox Proportional Hazards models to determine the impact of age, comorbidity, cancer risk, and race on survival. We stratified life expectancy estimates by age, comorbidity and cancer stage. RESULTS: Our analytic cohort included 145,678 patients. Survival modeling demonstrated the importance of age and comorbidity across all cancer risk categories. Life expectancy estimates generated from age and comorbidity data were predictive of overall survival (C-index 0.676, 95% CI 0.674-0.679) and visualized using Kaplan-Meier plots and heatmaps stratified by age and comorbidity. Separate life expectancy estimates were generated for patients with localized or advanced disease. These life expectancy estimates calibrate well across prostate cancer risk categories. CONCLUSIONS: Life expectancy estimates are essential to providing patient-centered prostate cancer care. We developed accessible life expectancy estimation tools for Veterans diagnosed with prostate cancer that can be used in routine clinical practice to inform medical-decision making.

Physician Usage and Acceptance of a Machine Learning Recommender System for Simulated Clinical Order Entry.

Clinical decision support tools that automatically disseminate patterns of clinical orders have the potential to improve patient care by reducing errors of omission and streamlining physician workflows. However, it is unknown if physicians will accept such tools or how their behavior will be affected. In this randomized controlled study, we exposed 34 licensed physicians to a clinical order entry interface and five simulated emergency cases, with randomized availability of a previously developed clinical order recommender system. With the recommender available, physicians spent similar time per case (6.7 minutes), but placed more total orders (17.1 vs. 15.8). The recommender demonstrated superior recall (59% vs 41%) and precision (25% vs 17%) compared to manual search results, and was positively received by physicians recognizing workflow benefits. Further studies must assess the potential clinical impact towards a future where electronic health records automatically anticipate clinical needs.

Receipt of Eye Care Services among Medicare Beneficiaries with and without Dementia.

PURPOSE: To examine the relationship between dementia status and receipt of eye care among US Medicare beneficiaries. DESIGN: Retrospective, claims-based analysis. PARTICIPANTS: A 20% representative sample of Medicare beneficiaries who received care between January 1, 2006, and December 31, 2015. METHODS: Dementia was identified from diagnosis codes documented in a beneficiary's first 3 years of observed Medicare enrollment. Eye care visits were identified from provider specialty codes on each encounter claim. We used multivariable Cox proportional hazards regression models with time-varying covariates to compare the likelihood of receiving eye care between beneficiaries with and without dementia. All models were adjusted for potential confounders, including demographics, urban/rural residence, systemic health (Charlson Index), and ocular comorbidities. MAIN OUTCOME MEASURES: Hazard ratio (HR) and 95% confidence interval (CI) for (1) being seen by any eye care provider (ophthalmologist or optometrist); (2) being seen by an ophthalmologist specifically; and (3) receiving cataract surgery (among beneficiaries with ophthalmologist encounters). RESULTS: A total of 4 451 200 beneficiaries met inclusion criteria; 3 805 718 (85.5%) received eye care during the study period, and 391 556 (8.8%) had diagnosed dementia. Some 73.4% of beneficiaries diagnosed with dementia saw an eye care provider during the study period and 55.4% saw an ophthalmologist versus 86.7% and 74.0% of beneficiaries, respectively, without dementia diagnoses. Compared with those without dementia diagnoses, beneficiaries with diagnosed dementia had lower likelihood of seeing any eye care provider (adjusted HR, 0.69; 95% CI, 0.69-0.70) and were less likely to see an ophthalmologist (adjusted HR, 0.55; 95% CI, 0.55-0.55). Among the subset of beneficiaries who did see ophthalmologists, those with diagnosed dementia were also less likely to receive cataract surgery than beneficiaries without diagnosed dementia (HR, 0.62; 95% CI, 0.62-0.63) and less likely to receive a cataract diagnosis (18% vs. 82%). CONCLUSIONS: US Medicare beneficiaries diagnosed with dementia are less likely to receive eye care than those without diagnosed dementia. Depending on visual acuity and functional status, this may have implications for injury prevention, physical and cognitive function, and quality of life. Further work is needed to identify barriers to receiving eye care, determine eye care services and settings that provide greatest value to patients with dementia, and implement measures to improve access to appropriate eye care.

"It's a disease of families": Neurologists' insights on how to improve communication and quality of life for families of Parkinson's disease patients.

OBJECTIVES: Parkinson's disease presents an evolving challenge for patients and families due to an unpredictable disease trajectory and symptoms that complicate social interactions. In this study, we explore neurologists' perspectives on the challenges Parkinson's disease presents for families and the strategies they use to improve communication and quality of life. METHODS: We conducted hour-long semi-structured interviews with 16 neurologists at 4 care delivery institutions in the San Francisco Bay Area, focusing on techniques neurologists use to support families through the Parkinson's disease journey. RESULTS: Neurologists identified strategies for addressing caregiver-patient disagreements around symptom accuracy and negotiating driving safety. Family education is needed to contextualize patient symptoms and to identify psychosocial support resources. Unmet caregiver needs remain, particularly in the form of psychosocial support, respite care and support for unequal gender dynamics in the Parkinson's disease caregiving experience. DISCUSSION: Family members of Parkinson's disease patients face unique caregiving and interpersonal challenges due to the nature of the disease. Targeted education and structural support are needed to alleviate current burdens and allow for improved patient- and family-centered care.

Technology use and preferences for mental health self-management interventions among older veterans.

OBJECTIVES: The United States Department of Veterans Affairs offers numerous technology-delivered interventions to self-manage mental health problems. It is unknown, however, what barriers older military veterans face to using these technologies and how willing they would be to use technologies for mental health concerns. METHODS: Seventy-seven veterans (Mage = 69.16 years; SD = 7.10) completed interviews in a concurrent mixed methods study. Interviewers asked about technology ownership and described four modalities of delivering self-management interventions: printed materials, DVDs, Internet, and mobile apps. Interviewers obtained feedback about each modality's benefits, barriers, and facilitators. Participants ranked their self-management modalities preferences alone and compared with counseling. Multivariable adjusted logistic regression and qualitative analyses were conducted to investigate the reasons contributing to preferences. RESULTS: Most reported owning a computer (84.4%), having home Internet (80.5%), and a smartphone (70.1%). Participants preferred printed materials (35.1%) over mobile apps (28.6%), Internet (24.7%), and DVDs (13.0%). Lower computer proficiency was associated with preferring DVDs; higher proficiency was associated with Internet and mobile interventions. Residing in an urban area was associated with mobile apps. When counseling was an option, 66% identified this as their first preference. Qualitative findings showed veterans' desire for information, training, and provider support with technology. CONCLUSIONS: Older veterans reported high technology ownership rates but varied preferences for self-management interventions. Notably, two-thirds preferred some form of technology, which points to the importance of ensuring that providers offer existing technology-delivered interventions to older veterans. Veterans' strong preference for counseling emphasizes the need for human support alongside self-management.

High-Need Patients' Goals and Goal Progress in a Veterans Affairs Intensive Outpatient Care Program.

BACKGROUND: Healthcare systems nationwide are implementing intensive outpatient care programs to optimize care for high-need patients; however, little is known about these patients' personal goals and factors associated with goal progress. OBJECTIVE: To describe high-need patients' goals, and to identify factors associated with their goal progress DESIGN: Retrospective cohort study PARTICIPANTS: A total of 113 high-need patients participated in a single-site Veterans Affairs intensive outpatient care program. MAIN MEASURES: Two independent reviewers examined patients' goals recorded in the electronic health record, categorized each goal into one of three domains (medical, behavioral, or social), and determined whether patients attained goal progress during program participation. Logistic regression was used to determine factors associated with goal progress. RESULTS: The majority (n = 72, 64%) of the 113 patients attained goal progress. Among the 100 (88%) patients with at least one identified goal, 58 set goal(s) in the medical domain; 60 in the behavioral domain; and 52 in the social domain. Within each respective domain, 41 (71%) attained medical goal progress; 34 (57%) attained behavioral goal progress; and 32 (62%) attained social goal progress. Patients with mental health condition(s) (aOR 0.3; 95% CI 0.1-0.9; p = 0.03) and those living alone (aOR 0.4; 95% CI 0.1-1.0; p = 0.05) were less likely to attain goal progress. Those with mental health condition(s) and those who were living alone were least likely to attain goal progress (interaction aOR 0.1 compared to those with neither characteristic; 95% CI 0.0-0.7; p = 0.02). CONCLUSIONS: Among high-need patients participating in an intensive outpatient care program, patient goals were fairly evenly distributed across medical, behavioral, and social domains. Notably, individuals living alone with mental health conditions were least likely to attain progress. Future care coordination interventions might incorporate strategies to address this gap, e.g., broader integration of behavioral and social service components.

An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes.

OBJECTIVE: Evaluate the quality of clinical order practice patterns machine-learned from clinician cohorts stratified by patient mortality outcomes. MATERIALS AND METHODS: Inpatient electronic health records from 2010 to 2013 were extracted from a tertiary academic hospital. Clinicians (n = 1822) were stratified into low-mortality (21.8%, n = 397) and high-mortality (6.0%, n = 110) extremes using a two-sided P-value score quantifying deviation of observed vs. expected 30-day patient mortality rates. Three patient cohorts were assembled: patients seen by low-mortality clinicians, high-mortality clinicians, and an unfiltered crowd of all clinicians (n = 1046, 1046, and 5230 post-propensity score matching, respectively). Predicted order lists were automatically generated from recommender system algorithms trained on each patient cohort and evaluated against (i) real-world practice patterns reflected in patient cases with better-than-expected mortality outcomes and (ii) reference standards derived from clinical practice guidelines. RESULTS: Across six common admission diagnoses, order lists learned from the crowd demonstrated the greatest alignment with guideline references (AUROC range = 0.86-0.91), performing on par or better than those learned from low-mortality clinicians (0.79-0.84, P < 10-5) or manually-authored hospital order sets (0.65-0.77, P < 10-3). The same trend was observed in evaluating model predictions against better-than-expected patient cases, with the crowd model (AUROC mean = 0.91) outperforming the low-mortality model (0.87, P < 10-16) and order set benchmarks (0.78, P < 10-35). DISCUSSION: Whether machine-learning models are trained on all clinicians or a subset of experts illustrates a bias-variance tradeoff in data usage. Defining robust metrics to assess quality based on internal (e.g. practice patterns from better-than-expected patient cases) or external reference standards (e.g. clinical practice guidelines) is critical to assess decision support content. CONCLUSION: Learning relevant decision support content from all clinicians is as, if not more, robust than learning from a select subgroup of clinicians favored by patient outcomes.

Selecting Test Cases from the Electronic Health Record for Software Testing of Knowledge-Based Clinical Decision Support Systems.

Software testing of knowledge-based clinical decision support systems is challenging, labor intensive, and expensive; yet, testing is necessary since clinical applications have heightened consequences. Thoughtful test case selection improves testing coverage while minimizing testing burden. ATHENA-CDS is a knowledge-based system that provides guideline-based recommendations for chronic medical conditions. Using the ATHENA-CDS diabetes knowledgebase, we demonstrate a generalizable approach for selecting test cases using rules/ filters to create a set of paths that mimics the system's logic. Test cases are allocated to paths using a proportion heuristic. Using data from the electronic health record, we found 1,086 cases with glycemic control above target goals. We created a total of 48 filters and 50 unique system paths, which were used to allocate 200 test cases. We show that our method generates a comprehensive set of test cases that provides adequate coverage for the testing of a knowledge-based CDS.

High-Risk Drug-Drug Interactions Between Clinical Practice Guidelines for Management of Chronic Conditions.

Clinicians and clinical decision-support systems often follow pharmacotherapy recommendations for patients based on clinical practice guidelines (CPGs). In multimorbid patients, these recommendations can potentially have clinically significant drug-drug interactions (DDIs). In this study, we describe and validate a method for programmatically detecting DDIs among CPG recommendations. The system extracts pharmacotherapy intervention recommendations from narrative CPGs, normalizes the terms, creates a mapping of drugs and drug classes, and then identifies occurrences of DDIs between CPG pairs. We used this system to analyze 75 CPGs written by authoring entities in the United States that discuss outpatient management of common chronic diseases. Using a reference list of high-risk DDIs, we identified 2198 of these DDIs in 638 CPG pairs (46 unique CPGs). Only 9 high-risk DDIs were discussed by both CPGs in a pairing. In 69 of the pairings, neither CPG had a pharmacologic reference or a warning of the possibility of a DDI.