Implementation of a Physical Activity Vital Sign in Primary Care: Associations Between Physical Activity, Demographic Characteristics, and Chronic Disease Burden.

INTRODUCTION: Physical activity is important to prevent and manage multiple chronic medical conditions. The objective of this study was to describe the implementation of a physical activity vital sign (PAVS) in a primary care setting and examine the association between physical activity with demographic characteristics and chronic disease burden. METHODS: We extracted data from the electronic medical records of patients who had visits from July 2018 through January 2020 in a primary care clinic in which PAVS was implemented as part of the intake process. Data collected included self-reported physical activity, age, sex, body mass index, race, ethnicity, and a modified Charlson Comorbidity Index score indicating chronic disease burden. We classified PAVS into 3 categories of time spent in moderate to strenuous intensity physical activity: consistently inactive (0 min/wk), inconsistently active (<150 min/wk), and consistently active (≥150 min/wk). We used χ2 tests of independence to test for association between PAVS categories and all other variables. RESULTS: During the study period, 13,704 visits, corresponding to 8,741 unique adult patients, had PAVS recorded. Overall, 18.1% of patients reported being consistently inactive, 48.3% inconsistently active, and 33.7% consistently active. All assessed demographic and clinical covariates were associated with PAVS classification (all P < .001). Larger percentages of consistent inactivity were reported for female, older, and underweight or obese patients. Larger percentages of consistent activity were reported for male, younger, and normal weight or overweight patients. CONCLUSION: Using PAVS as a screening tool in primary care enables physicians to understand the physical activity status of their patients and can be useful in identifying inactive patients who may benefit from physical activity counseling.

Transforming Population-Based Depression Care: a Quality Improvement Initiative Using Remote, Centralized Care Management.

INTRODUCTION: With the growing prevalence of value-based contracts, health systems are incentivized to consider population approaches to service delivery, particularly for chronic conditions like depression. To this end, UW Medicine implemented the Depression-Population Approach to Health (PATH) program in primary care (PC) as part of a system-wide Center for Medicare and Medicaid Innovation (CMMI) quality improvement (QI) initiative. AIM: To examine the feasibility of a pilot PATH program and its impact on clinical and process-of-care outcomes. SETTING: A large, diverse, geographically disparate academic health system in Western Washington State including 28 PC clinics across five networks. PROGRAM DESCRIPTION: The PATH program was a population-level, centralized, measurement-based care intervention that utilized a clinician to provide remote monitoring of treatment progress via chart review and facilitate patient engagement when appropriate. The primary goals of the program were to improve care engagement and increase follow-up PHQ-9 assessments for patients with depression and elevated initial PHQ-9 scores. PROGRAM EVALUATION: We employed a prospective, observational study design, including commercially insured adult patients with new depression diagnoses and elevated initial PHQ-9 scores. The pilot intervention group, consisting of accountable care network (ACN) self-enrollees (N = 262), was compared with a similar commercially insured cohort (N = 2527) using difference-in-differences analyses adjusted for patient comorbidities, initial PHQ-9 score, and time trends. The PATH program was associated with three times the odds of PHQ-9 follow-up (OR 3.28, 95% CI 1.79-5.99), twice the odds of a follow-up PC clinic visit (OR 1.74, 95% CI 0.99-3.08), and twice the odds of treatment response, defined as reduction in PHQ-9 score by ≥ 50% (OR 2.02, 95% CI 0.97-4.21). DISCUSSION: Our results demonstrate that a centralized, remote care management initiative is both feasible and effective for large academic health systems aiming to improve depression outcome ascertainment, treatment engagement, and clinical care.

Physical Activity in Adults With an Amputation as Assessed With a Self-Reported Exercise Vital Sign.

BACKGROUND: Physical activity (PA) is important for the prevention and treatment of numerous chronic medical conditions. Individuals with a limb amputation face unique challenges for staying physically active. There are few studies evaluating PA of civilians with amputation in the United States. OBJECTIVE: To evaluate self-reported PA in persons with an amputation in the outpatient setting using a standardized exercise vital sign (EVS) and correlate PA with demographic information, amputation characteristics, and disease burden. DESIGN: Cross-sectional observational study. SETTING: Outpatient rehabilitation clinic at a tertiary care institution. INTERVENTIONS: N/A. PARTICIPANTS: Two hundred twenty-nine patients with limb amputation. MAIN OUTCOME MEASUREMENTS: EVS (self-reported weekly participation in moderate to vigorous intensity exercise), disease burden using a modified Charlson Comorbidity Index (CCI), possession of a prosthetic limb, amputation level, time from amputation, body mass index (BMI), gender, race, and age. RESULTS: A total of 28.8% of patients with limb amputation self-reported exercising at or above 150 min/wk as recommended by the United States Department of Health and Human Services (HHS); 31.8% of patients with transfemoral amputations, 27.8% with transtibial amputations, and 36% with upper extremity amputations reported exercising the recommended amount. Those with a prosthesis exercised 0.91 h/wk more than those without a prosthesis (95% CI 0.01, 1.8, P = .047), and female patients exercised 1.09 h/wk less than male patients (95% confidence interval [CI] 1.69-0.49, P < .001). Increasing age (P = .045), CCI (P = .006), and BMI (P = .005) all had a small but significant correlation with lower EVS. There was no statistically significant correlation between EVS and amputation level, race, or time from amputation. CONCLUSIONS: Less than one-third of patients with an amputation meet HHS recommendations for aerobic exercise. Male patients, those with a prosthesis, lower CCI, lower BMI, and younger age reported higher PA rates. Assessing EVS can help clinicians to identify patients with amputation that are not sufficiently active and may benefit from PA counseling and prescription.

Identifying Patients Who Are Likely to Receive Most of Their Care From a Specific Health Care System: Demonstration via Secondary Analysis.

BACKGROUND: In the United States, health care is fragmented in numerous distinct health care systems including private, public, and federal organizations like private physician groups and academic medical centers. Many patients have their complete medical data scattered across these several health care systems, with no particular system having complete data on any of them. Several major data analysis tasks such as predictive modeling using historical data are considered impractical on incomplete data. OBJECTIVE: Our objective was to find a way to enable these analysis tasks for a health care system with incomplete data on many of its patients. METHODS: This study presents, to the best of our knowledge, the first method to use a geographic constraint to identify a reasonably large subset of patients who tend to receive most of their care from a given health care system. A data analysis task needing relatively complete data can be conducted on this subset of patients. We demonstrated our method using data from the University of Washington Medicine (UWM) and PreManage data covering the use of all hospitals in Washington State. We compared 10 candidate constraints to optimize the solution. RESULTS: For UWM, the best constraint is that the patient has a UWM primary care physician and lives within 5 miles of at least one UWM hospital. About 16.01% (55,707/348,054) of UWM patients satisfied this constraint. Around 69.38% (10,501/15,135) of their inpatient stays and emergency department visits occurred within UWM in the following 6 months, more than double the corresponding percentage for all UWM patients. CONCLUSIONS: Our method can identify a reasonably large subset of patients who tend to receive most of their care from UWM. This enables several major analysis tasks on incomplete medical data that were previously deemed infeasible.

Issues in biomedical research data management and analysis: needs and barriers.

OBJECTIVES: A. Identify the current state of data management needs of academic biomedical researchers. B. Explore their anticipated data management and analysis needs. C. Identify barriers to addressing those needs. DESIGN: A multimodal needs analysis was conducted using a combination of an online survey and in-depth one-on-one semi-structured interviews. Subjects were recruited via an e-mail list representing a wide range of academic biomedical researchers in the Pacific Northwest. MEASUREMENTS: The results from 286 survey respondents were used to provide triangulation of the qualitative analysis of data gathered from 15 semi-structured in-depth interviews. RESULTS: Three major themes were identified: 1) there continues to be widespread use of basic general-purpose applications for core data management; 2) there is broad perceived need for additional support in managing and analyzing large datasets; and 3) the barriers to acquiring currently available tools are most commonly related to financial burdens on small labs and unmet expectations of institutional support. CONCLUSION: Themes identified in this study suggest that at least some common data management needs will best be served by improving access to basic level tools such that researchers can solve their own problems. Additionally, institutions and informaticians should focus on three components: 1) facilitate and encourage the use of modern data exchange models and standards, enabling researchers to leverage a common layer of interoperability and analysis; 2) improve the ability of researchers to maintain provenance of data and models as they evolve over time though tools and the leveraging of standards; and 3) develop and support information management service cores that could assist in these previous components while providing researchers with unique data analysis and information design support within a spectrum of informatics capabilities.

Examining Researcher Needs and Barriers for using Electronic Health Data for Translational Research.

To achieve the Learning Health Care System, we must harness electronic health data (EHD) by providing effective tools for researchers to access data efficiently. EHD is proliferating and researchers are relying on these data to pioneer discovery. Tools must be user-centric to ensure their utility. To this end, we conducted a qualitative study to assess researcher needs and barriers to using EHD. Researchers expressed the need to be confident about the data and have easy access, a clear process for exploration and access, and adequate resources, while barriers included difficulties in finding datasets, usability of the data, cumbersome processes, and lack of resources. These needs and barriers can inform the design process for innovating tools to increase utility of EHD. Understanding researcher needs is key to building effective user-centered EHD tools to support translational research.

Characterizing Secondary Use of Clinical Data.

The increasing reliance on electronic health data has created new opportunities for the secondary use of clinical data to impact practice. We analyzed the secondary uses of clinical data at the University of Washington (UW) to better understand the types of users and uses as well as the benefits and limitations of these electronic data. At the UW, a diverse population is utilizing different elements of clinical data to conduct a wide· variety of studies. Investigators are using clinical data to explore research questions, determine study feasibility and to reduce the burden of manual chart abstraction. Discovered limitations include difficult-to-use data formatting, researchers' lack of understanding about the data structure and organization resulting in mistrust, and difficulty generalizing data to fit needs of many specialized users.

Incorporating collaboratory concepts into informatics in support of translational interdisciplinary biomedical research.

Due to its complex nature, modern biomedical research has become increasingly interdisciplinary and collaborative in nature. Although a necessity, interdisciplinary biomedical collaboration is difficult. There is, however, a growing body of literature on the study and fostering of collaboration in fields such as computer supported cooperative work (CSCW) and information science (IS). These studies of collaboration provide insight into how to potentially alleviate the difficulties of interdisciplinary collaborative research. We, therefore, undertook a cross cutting study of science and engineering collaboratories to identify emergent themes. We review many relevant collaboratory concepts: (a) general collaboratory concepts across many domains: communication, common workspace and coordination, and data sharing and management, (b) specific collaboratory concepts of particular biomedical relevance: data integration and analysis, security structure, metadata and data provenance, and interoperability and data standards, (c) environmental factors that support collaboratories: administrative and management structure, technical support, and available funding as critical environmental factors, and (d) future considerations for biomedical collaboration: appropriate training and long-term planning. In our opinion, the collaboratory concepts we discuss can guide planning and design of future collaborative infrastructure by biomedical informatics researchers to alleviate some of the difficulties of interdisciplinary biomedical collaboration.

Automatic XQuery generation and generalized visualization for an XML interface to a relational database.

XBrain is an application that facilitates data exchange by dynamically publishing relational data over the web in XML format. In the current work we further enhanced its functionality through automatic query generation to aid the human users. We also extended its visualization tools by developing them into generalized XML visualization web services.