Robin Clarke

Risk factors for thrombocytopenia in HIV-infected persons in the era of potent antiretroviral therapy.

Objective:Before potent antiretroviral therapy, thrombocytopenia was observed frequently. Little is known about risk factors for or severity and consequences of thrombocytopenia since establishment of highly effective therapy for HIV. Methods:We conducted a retrospective-matched case-control study of HIV-infected adult outpatients with and without thrombocytopenia to elucidate the contribution of HIV viremia, hepatitis C infection, and other potential risk factors for thrombocytopenia. Seventy-three cases with thrombocytopenia (platelet count <100 × 109/L persistent for >3 months) were matched by age, sex, and first clinic visit with 73 nonthrombocytopenic controls. Risk factors and outcomes were assessed using conditional logistic regression. Results:Nadir platelet counts in cases were ≤50 × 109/L in 58% and ≤30 × 109/L in 38%. In multivariate modeling, HIV RNA >400 copies/ml, hepatitis C virus infection, and cirrhosis were significantly associated with thrombocytopenia with adjusted odds ratios of 5.3 [confidence interval (CI) 1.6-17.1, P = 0.006], 6.1 (CI 1.6-22.6, P = 0.007), and 24.0 (CI 1.7-338, P = 0.019), respectively. Thrombocytopenia was significantly associated with major bleeding events and nonbleeding-related death. Conclusions:Thrombocytopenia in the era of potent antiretroviral therapy is associated with hepatitis C virus infection, cirrhosis, and uncontrolled HIV replication, and serious complications including major bleeding and death.

Tool used to assess how well community health centers function as medical homes may be flawed.

The patient-centered medical home model holds the potential for reducing disease complications and improving health, and the federal government is now promoting the adoption of the model within federally qualified community health centers. In a group of Los Angeles community health centers, we found that all would have qualified as patient-centered medical homes under a widely used assessment tool developed by the National Committee for Quality Assurance and endorsed by the federal government for the community health center program. However, we also found that there was no significant relationship between how well these centers performed on the assessment and whether they achieved a range of process or outcome measures for diabetes care. These findings suggest that the federal government is promoting medical home redesign that may not be sensitive to, or inclusive of, services that will actually improve diabetes care for low-income patients. Therefore, additional methods are required for measuring and improving the capabilities of community health centers to function as medical homes and to deliver the scope of services that impoverished patients genuinely need.

Effect of Closure of a Local Safety-Net Hospital on Primary Care Physicians’ Perceptions of Their Role in Patient Care

PURPOSE We examined how the closure of a large safety-net hospital in Los Angeles County, California, affected local primary care physicians. METHODS We conducted semistructured interviews with 42 primary care physicians who practiced in both underserved and nonunderserved settings in Los Angeles County. Two investigators independently reviewed and coded transcripts. Three investigators used pile-sorting to sort the codes into themes. RESULTS Overall, 28 of 42 physicians (67%) described some effect of the hospital closure on their practices. Three major themes emerged regarding the impact of the closure on the affected physicians: (1) reduced local access to specialist consultations, direct hospital admissions, and timely emergency department evaluation; (2) more patient delays in care and worse health outcomes because of poor patient understanding of the health care system changes; and (3) loss of colleagues and opportunities to teach residents and medical students. CONCLUSIONS Physicians in close proximity to the closed hospital—even those practicing in nonunderserved settings—reported difficulty getting their patients needed care that extended beyond the anticipated loss of inpatient services. There is a need for greater recognition of and support for the role primary care physicians play in coordinating care; promoting continuity of care; and informing patients, clinic administrators and policy makers about system changes during such transitions.

Testing an Adapted Modified Delphi Method: Synthesizing Multiple Stakeholder Ratings of Health Care Service Effectiveness

Background. The Affordable Care Act incentivizes health systems for better meeting patient needs, but often guidance about patient preferences for particular health services is limited. All too often vulnerable patient populations are excluded from these decision-making settings. A community-based participatory approach harnesses the in-depth knowledge of those experiencing barriers to health care. Method. We made three modifications to the RAND-UCLA appropriateness method, a modified Delphi approach, involving patients, adding an advisory council group to characterize existing knowledge in this little studied area, and using effectiveness rather than “appropriateness” as the basis for rating. As a proof of concept, we tested this method by examining the broadly delivered but understudied nonmedical services that community health centers provide. Results. This method created discrete, new knowledge about these services by defining 6 categories and 112 unique services and by prioritizing among these services based on effectiveness using a 9-point scale. Consistent with the appropriateness method, we found statistical convergence of ratings among the panelists. Discussion. Challenges include time commitment and adherence to a clear definition of effectiveness of services. This diverse stakeholder engagement method efficiently addresses gaps in knowledge about the effectiveness of health care services to inform population health management.

Defining and Rating the Effectiveness of Enabling Services Using a Multi-stakeholder Expert Panel Approach

The Affordable Care Act provides opportunities to reimburse non-medical enabling services that promote the delivery of medical care for patients with social barriers. However, limited evidence exists to guide delivery of these services. We addressed this gap by convening community health center patients, providers, and other stakeholders in two panels that developed a framework for defining and evaluating these services. We adapted a group consensus method where the panelists rated services for effectiveness in increasing access to, use, and understanding of medical care. Panelists defined six broad categories, 112 services, and 21 variables including the type of provider delivering the service. We identified 16 highest-rated services and found that the service provider’s level of training affected effectiveness for some but not all services. In a field with little evidence, these findings provide guidance to decision-makers for the targeted spread of services that enable patients to overcome social barriers to care.

Building the Infrastructure for Value at UCLA: Engaging Clinicians and Developing Patient-Centric Measurement.

PROBLEM Evolving payer and patient expectations have challenged academic health centers (AHCs) to improve the value of clinical care. Traditional quality approaches may be unable to meet this challenge. APPROACH One AHC, UCLA Health, has implemented a systematic approach to delivery system redesign that emphasizes clinician engagement, a patient-centric scope, and condition-specific, clinician-guided measurement. A physician champion serves as quality officer (QO) for each clinical department/division. Each QO, with support from a central measurement team, has developed customized analytics that use clinical data to define targeted populations and measure care across the full treatment episode. OUTCOMES From October 2012 through June 2015, the approach developed rapidly. Forty-three QOs are actively redesigning care delivery protocols within their specialties, and 95% of the departments/divisions have received a customized measure report for at least one patient population. As an example of how these analytics promote systematic redesign, the authors discuss how Department of Urology physicians have used these new measures, first, to better understand the relationship between clinical practice and outcomes for patients with benign prostatic hyperplasia and, then, to work toward reducing unwarranted variation. Physicians have received these efforts positively. Early outcome data are encouraging. NEXT STEPS This infrastructure of engaged physicians and targeted measurement is being used to implement systematic care redesign that reliably achieves outcomes that are meaningful to patients and clinicians-incorporating both clinical and cost considerations. QOs are using an approach, for multiple newly launched projects, to identify, test, and implement value-oriented interventions tailored to specific patient populations.

Defining a Patient Population With Cirrhosis: An Automated Algorithm With Natural Language Processing.

Objectives: The objective of this study was to use natural language processing (NLP) as a supplement to International Classification of Diseases, Ninth Revision (ICD-9) and laboratory values in an automated algorithm to better define and risk-stratify patients with cirrhosis. Background: Identification of patients with cirrhosis by manual data collection is time-intensive and laborious, whereas using ICD-9 codes can be inaccurate. NLP, a novel computerized approach to analyzing electronic free text, has been used to automatically identify patient cohorts with gastrointestinal pathologies such as inflammatory bowel disease. This methodology has not yet been used in cirrhosis. Study Design: This retrospective cohort study was conducted at the University of California, Los Angeles Health, an academic medical center. A total of 5343 University of California, Los Angeles primary care patients with ICD-9 codes for chronic liver disease were identified during March 2013 to January 2015. An algorithm incorporating NLP of radiology reports, ICD-9 codes, and laboratory data determined whether these patients had cirrhosis. Of the 5343 patients, 168 patient charts were manually reviewed at random as a gold standard comparison. Positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity of the algorithm and each of its steps were calculated. Results: The algorithm’s PPV, NPV, sensitivity, and specificity were 91.78%, 96.84%, 95.71%, and 93.88%, respectively. The NLP portion was the most important component of the algorithm with PPV, NPV, sensitivity, and specificity of 98.44%, 93.27%, 90.00%, and 98.98%, respectively. Conclusions: NLP is a powerful tool that can be combined with administrative and laboratory data to identify patients with cirrhosis within a population.

Development of a high-value care culture survey: a modified Delphi process and psychometric evaluation

Background Organisational culture affects physician behaviours. Patient safety culture surveys have previously been used to drive care improvements, but no comparable survey of high-value care culture currently exists. We aimed to develop a High-Value Care Culture Survey (HVCCS) for use by healthcare leaders and training programmes to target future improvements in value-based care. Methods We conducted a two-phase national modified Delphi process among 28 physicians and nurse experts with diverse backgrounds. We then administered a cross-sectional survey at two large academic medical centres in 2015 among 162 internal medicine residents and 91 hospitalists for psychometric evaluation. Results Twenty-six (93%) experts completed the first phase and 22 (85%) experts completed the second phase of the modified Delphi process. Thirty-eight items achieved ≥70% consensus and were included in the survey. One hundred and forty-one residents (83%) and 73 (73%) hospitalists completed the survey. From exploratory factor analyses, four factors emerged with strong reliability: (1) leadership and health system messaging (α=0.94); (2) data transparency and access (α=0.80); (3) comfort with cost conversations (α=0.70); and (4) blame-free environment (α=0.70). In confirmatory factor analysis, this four-factor model fit the data well (Bentler-Bonett Normed Fit Index 0.976 and root mean square residual 0.056). The leadership and health system messaging (r=0.56, p<0.001), data transparency and access (r=0.15, p<0.001) and blame-free environment (r=0.37, p<0.001) domains differed significantly between institutions and positively correlated with Value-Based Purchasing Scores. Conclusions Our results provide support for the reliability and validity of the HVCCS to assess high-value care culture among front-line clinicians. HVCCS may be used by healthcare groups to identify target areas for improvements and to monitor the effects of high-value care initiatives.

Development and Validation of an Automated Method to Identify Patients Undergoing Radical Cystectomy for Bladder Cancer Using Natural Language Processing

Introduction: Measurement for quality improvement relies on accurate case identification and characterization. With electronic health records now widely deployed, natural language processing, the use of software to transform text into structured data, may enrich quality measurement. Accordingly we evaluated the application of natural language processing to radical cystectomy procedures for patients with bladder cancer. Methods: From a sample of 497 procedures performed from March 2013 to October 2014 we identified radical cystectomy for primary bladder cancer using the approaches of 1) a natural language processing enhanced algorithm, 2) an administrative claims based algorithm and 3) manual chart review. We also characterized treatment with robotic surgery and continent urinary diversion. Using chart review as the reference standard we calculated the observed agreement (kappa statistic), sensitivity, specificity, positive predictive value and negative predictive value for natural language processing and administrative claims. Results: We confirmed 84 radical cystectomies were performed for bladder cancer, with 50.0% robotic and 38.6% continent diversions. The natural language processing enhanced and claims based algorithms demonstrated 99.8% (&kgr;=0.993, 95% CI 0.979–1.000) and 98.6% (&kgr;=0.951, 95% CI 0.915–0.987) agreement with manual review, respectively. Both approaches accurately characterized robotic vs open surgery, with natural language processing enhanced algorithms showing 98.8% (&kgr;=0.976, 95% CI 0.930–1.000) and claims based 90.5% (&kgr;=0.810, 95% CI 0.686–0.933) agreement. For urinary diversion natural language processing enhanced algorithms correctly specified 96.4% of cases (&kgr;=0.924, 95% CI 0.839–1.000) compared with 83.3% (&kgr;=0.655, 95% CI 0.491–0.819). Conclusions: Natural language processing enhanced and claims based algorithms accurately identified radical cystectomy cases at our institution. However, natural language processing appears to better classify specific aspects of cystectomy surgery, highlighting a potential advantage of this emerging methodology.

Predictors of Out-of-ACO Care in the Medicare Shared Savings Program.

Importance:Patients treated outside of their Medicare Shared Savings Program (MSSP) accountable care organization (ACO) likely benefit less from the ACO’s integration of care. Consequently, the MSSP’s open-network design may preclude ACOs from improving value in care. Objectives:Quantify out-of-ACO care in a single urban ACO and examine associations between patient-level predictors and out-of-ACO expenditures. Research Design:Secondary data analysis using Centers for Medicare and Medicaid ACO Program Claim and Claim Line Feed dataset (dates of service January 1, 2013–December 31, 2013). Two-part modeling was used to examine associations between patient-level predictors and likelihood and level of out-of-ACO expenditures. Subjects:Patients were included if they were prospectively assigned to the MSSP in 2013. Patients were excluded if they declined to share data with the ACO, were not retrospectively confirmed to be in the ACO, or had missing data on covariates. Analytic sample included 11,922 patients. Measures:Total out-of-ACO expenditures and out-of-ACO expenditures by place of service. Results:Of total expenditures, 32.9% were paid to out-of-ACO providers, and 89.8% of beneficiaries had out-of-ACO expenditures. The presence of almost all medical comorbidities increased out-of-ACO expenditures (