Nowella Durkin

ASSOCIATION OF SELF-REPORTED HOSPITAL DISCHARGE HANDOFFS WITH 30-DAY READMISSIONS

IMPORTANCE Poor health care provider communication across health care settings may lead to adverse outcomes. OBJECTIVE To determine the frequency with which inpatient providers report communicating directly with outpatient providers and whether direct communication was associated with 30-day readmissions. DESIGN We conducted a single-center prospective study of self-reported communication patterns by discharging health care providers on inpatient medical services from September 2010 to December 2011 at The Johns Hopkins Hospital. SETTING A 1000-bed urban, academic center. PARTICIPANTS There were 13 954 hospitalizations in this time period. Of those, 9719 were for initial visits. After additional exclusions, including patients whose outpatient health care provider was the inpatient attending physician, those who had planned or routine admissions, those without outpatient health care providers, those who died in the hospital, and those discharged to other healthcare facilities, we were left with 6635 hospitalizations for analysis. INTERVENTIONS Self-reported communication was captured from a mandatory electronic discharge worksheet field. Thirty-day readmissions, length of stay (LOS), and demographics were obtained from administrative databases. DATA EXTRACTION We used multivariable logistic regression models to examine, first, the association between direct communication and patient age, sex, LOS, race, payer, expected 30-day readmission rate based on diagnosis and illness severity, and physician type and, second, the association between 30-day readmission and direct communication, adjusting for patient and physician-level factors. RESULTS Of 6635 included hospitalizations, successful direct communication occurred in 2438 (36.7%). The most frequently reported reason for lack of direct communication was the health care providers perception that the discharge summary was adequate. Predictors of direct communication, adjusting for all other variables, included patients cared for by hospitalists without house staff (odds ratio [OR], 1.81 [95% CI, 1.59-2.08]), high expected 30-day readmission rate (OR, 1.18 [95% CI, 1.10-1.28] per 10%), and insurance by Medicare (OR, 1.35 [95% CI, 1.16-1.56]) and private insurance companies (OR, 1.35 [95% CI, 1.18-1.56]) compared with Medicaid. Direct communication with the outpatient health care provider was not associated with readmissions (OR, 1.08 [95% CI, 0.92-1.26]) in adjusted analysis. CONCLUSIONS AND RELEVANCE Self-reported direct communication between inpatient and outpatient providers occurred at a low rate but was not associated with readmissions. This suggests that enhancing interprovider communication at hospital discharge may not, in isolation, prevent readmissions.

Longitudinal Accuracy of Web-Based Self-Reported Weights: Results From the Hopkins POWER Trial

Background Websites and phone apps are increasingly used to track weights during weight loss interventions, yet the longitudinal accuracy of these self-reported weights is uncertain. Objective Our goal was to compare the longitudinal accuracy of self-reported weights entered online during the course of a randomized weight loss trial to measurements taken in the clinic. We aimed to determine if accuracy of self-reported weight is associated with weight loss and to determine the extent of misclassification in achieving 5% weight loss when using self-reported compared to clinic weights. Methods This study examined the accuracy of self-reported weights recorded online among intervention participants in the Hopkins Practice-Based Opportunities for Weight Reduction (POWER) trial, a randomized trial examining the effectiveness of two lifestyle-based weight loss interventions compared to a control group among obese adult patients with at least one cardiovascular risk factor. One treatment group was offered telephonic coaching and the other group was offered in-person individual coaching and group sessions. All intervention participants (n=277) received a digital scale and were asked to track their weight weekly on a study website. Research staff used a standard protocol to measure weight in the clinic. Differences (self-reported weight – clinic weight) indicate if self-report under (-) or over (+) estimated clinic weight using the self-reported weight that was closest in time to the clinic weight and was within a window ranging from the day of the clinic visit to 7 days before the 6-month (n=225) and 24-month (n=191) clinic visits. The absolute value of the differences (absolute difference) describes the overall accuracy. Results Underestimation of self-reported weights increased significantly from 6 months (mean -0.5kg, SD 1.0kg) to 24 months (mean -1.1kg, SD 2.0kg; P=.002). The average absolute difference also increased from 6 months (mean 0.7kg, SD 0.8kg) to 24 months (mean 1.3, SD 1.8kg; P<.001). Participants who achieved the study weight loss goal at 24 months (based on clinic weights) had lower absolute differences (P=.01) compared to those who did not meet this goal. At 24 months, there was 9% misclassification of weight loss goal success when using self-reported weight compared to clinic weight as an outcome. At 24 months, those with self-reported weights (n=191) had three times the weight loss compared to those (n=73) without self-reported weights (P<.001). Conclusions Underestimation of weight increased over time and was associated with less weight loss. In addition to intervention adherence, weight loss programs should emphasize accuracy in self-reporting. Trial Registration ClinicalTrials.gov: NCT00783315; http://clinicaltrials.gov/show/NCT00783315 (Archived by WebCite at http://www.webcitation.org/6R4gDAK5K).

Willingness to pay for continued delivery of a lifestyle-based weight loss program: The Hopkins POWER trial

In behavioral studies of weight loss programs, participants typically receive interventions free of charge. Understanding an individuals willingness to pay (WTP) for weight loss programs could be helpful when evaluating potential funding models. This study assessed WTP for the continuation of a weight loss program at the end of a weight loss study.

A Method for Attributing Patient-Level Metrics to Rotating Providers in an Inpatient Setting

BACKGROUND Individual provider performance drives group metrics, and increasingly, individual providers are held accountable for these metrics. However, appropriate attribution can be challenging, particularly when multiple providers care for a single patient. OBJECTIVE We sought to develop and operationalize individual provider scorecards that fairly attribute patient-level metrics, such as length of stay and patient satisfaction, to individual hospitalists involved in each patient’s care. DESIGN Using patients cared for by hospitalists from July 2010 through June 2014, we linked billing data across each hospitalization to assign “ownership” of patient care based on the type, timing, and number of charges associated with each hospitalization (referred to as “provider day weighted”). These metrics were presented to providers via a dashboard that was updated quarterly with their performance (relative to their peers). For the purposes of this article, we compared the method we used to the traditional method of attribution, in which an entire hospitalization is attributed to 1 provider, based on the attending of record as labeled in the administrative data. RESULTS Provider performance in the 2 methods was concordant 56% to 75% of the time for top half versus bottom half performance (which would be expected to occur by chance 50% of the time). While provider percentile differences between the 2 methods were modest for most providers, there were some providers for whom the methods yielded dramatically different results for 1 or more metrics. CONCLUSION We found potentially meaningful discrepancies in how well providers scored (relative to their peers) based on the method used for attribution. We demonstrate that it is possible to generate meaningful provider-level metrics from administrative data by using billing data even when multiple providers care for 1 patient over the course of a hospitalization.

Perceived helpfulness of the individual components of a behavioural weight loss program: results from the Hopkins POWER Trial

Behavioural weight loss programs are effective first‐line treatments for obesity and are recommended by the US Preventive Services Task Force. Gaining an understanding of intervention components that are found helpful by different demographic groups can improve tailoring of weight loss programs. This paper examined the perceived helpfulness of different weight loss program components.

From efficacy to effectiveness: lessons learned from the Practice-Based Opportunities for Weight Reduction (POWER) trial

Despite the encouraging results of behavioral weight-loss interventions in effi cacy trials, primary care providers (PCPs) have expressed a lack of confidence that proven weight management models can be implemented in a primary care setting [1]. The Practice-Based Opportunities for Weight Reduction (POWER) trial conducted at Johns Hopkins University (MD, USA) compared the effectiveness of two behavioral weight-loss programs to a self-directed control group over 24 months among obese adults with at least one cardiovascular disease risk factor. In contrast to traditional efficacy studies that often enrolled convenience popula tions, POWER enrolled obese patients from primary care clinics and delivered interventions that could potentially be integrated into routine medical care. As a comparative effectiveness trial, our findings should be directly applicable to rou tine medical care and should inform patients, healthcare providers and delivery systems of effective treatment options [2]. This article highlights lessons learned from the design and implementation of the POWER trial. Details of the study design have been published [3,4]. Participants were obese patients from one of six participating primary care clinics, who had at least one cardiovascular risk factor (hypertension, hypercholesterolemia, or diabetes), regular internet access and basic computer skills (i.e., the ability to enter data into a website and send/receive email). Participants were randomized to one of three groups: control condition, remote intervention and in-person intervention. The primary outcome was weight loss at 24 months after randomization. The two behavioral interventions were based on previous trials conducted by the Hopkins investigative team that demonstrated the efficacy of lifestyle interventions in achieving and maintaining weight loss and improving cardiovascular disease risk factors [5–8]. The group receiving in-person support was offered group sessions, individual sessions (telephonic and in-person) and web support. The group receiving remote support was provided telephonic sessions and web support with no face-to-face contact. These active intervention groups were compared with the self-directed control group. A summary of the intervention recommendations and contact schedule have been published [3,4]. PCPs provided general encouragement for intervention participation and reviewed a one-page report of the participant’s progress at regularly scheduled clinic visits for those in the active interventions. The main results have been published and a brief review follows [4]. Of the 415 randomized participants, 64% were women and 41% were African–American; mean (standard deviation) age was 54 (10.2) years, and mean (standard deviation) BMI was 36.6 (5.4) kg/m 2 . The median number of completed coaching sessions in the remote support arm was 14 during the first 6 months (15 sessions were offered), 1

Factors associated with early dropout in an employer-based commercial weight-loss program: Weight-loss program early dropout

Minimizing program dropout is essential for weight‐loss success, but factors that influence dropout among commercial programs are unclear. This studys objective was to determine factors associated with early dropout in a commercial weight‐loss program.

Geographically Localized Medicine House-Staff Teams and Patient Satisfaction:

Background: Geographically localized care teams may demonstrate improved communication between team members and patients, potentially enhancing coordination of care. However, the impact of geographically localized team on patient experience scores is not well understood. Objective: To compare experience scores of patients on resident teams home clinical units with patients assigned to them off of their home units over a 10-year period. Participants: Patients admitted to any of the 4 chief resident staffed internal medicine inpatient service were included. Patients admitted to the house-staff teams’ home clinical unit comprised the exposure group and their patients off of their home units comprised the control patients. Measurement: Top-box experience scores calculated from the physician Hospital Consumer Assessment of Healthcare and Provider Systems (HCAHPS) and Press Ganey patient satisfaction surveys. Results: There were 3012 patients included in the study. There were no significant differences in experience scores with physician communication, nursing communication, pain, or discharge planning between the 2 groups. Patients did not report satisfaction more often with the time physicians spent with them on localized teams (48.6% vs 47.5%; P = .54) or that staff were better at working together (63.2% vs 61.3%; P = .29). This did not change during a 45-month period when the proportion of patients on home units exceeded 75% and multidisciplinary rounds were started. Conclusion: Patients cared for by geographically localized teams did not have better patient experience. Other factors such as physician communication skills or limited time spent in direct care may overshadow the impact of having localized teams. Further research is needed to better understand organizational, team, and individual factors impacting patient experience.

Patient Perceptions of Readmission Risk: An Exploratory Survey

Interventions to prevent readmissions often rely upon patient participation to be successful. We surveyed 895 general medicine patients slated for hospital discharge to (1) assess patient attitudes surrounding readmission, (2) ascertain whether these attitudes were associated with actual readmission, and (3) determine whether patients can estimate their own readmission risk. Actual readmissions and other clinical variables were captured from administrative data and linked to individual survey responses. We found that actual readmissions were not correlated with patients’ interest in preventing readmission, sense of control over readmission, or intent to follow discharge instructions. However, patients were able to predict their own readmissions (P = .005) even after adjusting for predicted readmission rate, race, sex, age, and payer. Reassuringly, over 80% of respondents reported that they would be frustrated or disappointed to be readmitted and almost 90% indicated that they planned to follow all of their discharge instructions. Whether assessing patient-perceived readmission risk might help to target preventive interventions warrants further study.

Does Patient Experience Predict 30-Day Readmission? A Patient-Level Analysis of HCAHPS Data

BACKGROUND Hospital-level studies have found an inverse relationship between patient experience and readmissions. However, based on typical survey response time, it is unclear if patients are able to respond to surveys before they get readmitted and whether being readmitted might be a driver of poor experience scores (reverse causation). OBJECTIVE Using patient-level Hospital Consumer Assessment of Healthcare Providers and Systems (HCHAPS) and Press Ganey data to examine the relationship between readmissions and experience scores and to distinguish between patients who responded before or after a subsequent readmission. DESIGN Retrospective analysis of 10-year HCAHPS data. SETTING Single tertiary care academic hospital. PARTICIPANTS Patients readmitted within 30 days of an index hospitalization who received an HCAHPS survey linked to index admission comprised the exposure group. This group was divided into those who responded prior to readmission and those who responded after readmission. Nonreadmitted patients comprised the control group. ANALYSIS Multivariable-logistic regression to analyze the association between HCHAPS and Press Ganey scores and 30-readmission status, adjusted for patient factors. RESULTS Only 15.8% of the readmitted patients responded to the survey prior to readmission, and their scores were not significantly different from the nonreadmitted patients. The patients who responded after readmission were significantly more dissatisfied with physicians (doctors listened 73.0% versus 79.2%, adjusted odds ratio [aOR] 0.75, P < .0001), staff responsiveness, (call button 50.0% vs 59.1%, aOR 0.71, P < .0001) pain control, discharge plan, noise, and cleanliness of the hospital. CONCLUSION Our findings suggest that poor patient experience, may be due to being readmitted, rather than being predictive of readmission.