Patient Perspectives on Care Transitions From Hospital to Home.

Importance: Understanding the patient's perspective of their care transition process from hospital or skilled nursing facility (SNF) to home may highlight gaps in care and inform system improvements. Objective: To gather data about patients' care transition experiences and factors associated with follow-up appointment completion. Design, Setting, and Participants: A survey tool was developed with input from patient advisors and organizations participating in a collaborative quality initiative. Seventeen hospitals, 12 practitioner organizations, and 6 SNFs in Michigan collaborated to identify shared patients who were aged 18 years and older, had a working telephone number, recently returned home or to an assisted living facility with a diagnosis of congestive heart failure or chronic obstructive pulmonary disease, or after an SNF stay. Using consecutive sampling, interviewers collected 5 telephone surveys per month. From October 2018 to December 2019, patients or caregivers were surveyed via telephone 8 to 12 days after discharge from a hospital or SNF. Data were analyzed from March 2020 to January 2022. Exposure: Care transition experiences. Main Outcomes and Measures: The primary outcome was to identify patient-perceived gaps during care transition experiences, including postdischarge follow-up. Results: On the basis of pilot data, the response rate was estimated at 34%, yielding 1257 surveys. Of 1257 survey respondents (mean [SD] age, 70 [12.94] years for 968 patients for whom age data was available), 654 (52%) were female; 829 (74%) were White, 250 (22%) were Black or African American, and 40 (4%) were another race. Eleven percent of patients reported not receiving a telephone number to call for postdischarge questions. Nearly 80% of patients (977 patients) received a follow-up telephone call, and most found it valuable. Twenty percent of patients (255 patients) reported at least 1 social determinant of health issue. Lack of transportation was associated with reduced likelihood of completing a follow-up visit, decreasing the odds of completing a follow-up by nearly 70% (odds ratio [OR], 0.31; 95% CI, 0.18-0.53; P < .001). Compared with other patient groups, Black patients were less likely to report completing a postdischarge follow-up visit (OR, 0.49; 95% CI, 0.36-0.67; P < .001) or to receive prescribed medical equipment (OR, 4.23; 95% CI, 1.30-13.83; P = .02). Conclusions and Relevance: An examination of patient discharge experiences from a hospital or SNF identified inconsistencies in care transition processes, social determinants of health issues needing to be addressed after discharge, and racial disparities between patients who attend follow-up appointments. Physicians should be aware of these findings and their consequences for patient experiences.

Creating an effort tracking tool to improve therapeutic cancer clinical trials workload management and budgeting.

Quantifying data management and regulatory workload for clinical research is a difficult task that would benefit from a robust tool to assess and allocate effort. As in most clinical research environments, The University of Michigan Comprehensive Cancer Center (UMCCC) Clinical Trials Office (CTO) struggled to effectively allocate data management and regulatory time with frequently inaccurate estimates of how much time was required to complete the specific tasks performed by each role. In a dynamic clinical research environment in which volume and intensity of work ebbs and flows, determining requisite effort to meet study objectives was challenging. In addition, a data-driven understanding of how much staff time was required to complete a clinical trial was desired to ensure accurate trial budget development and effective cost recovery. Accordingly, the UMCCC CTO developed and implemented a Web-based effort-tracking application with the goal of determining the true costs of data management and regulatory staff effort in clinical trials. This tool was developed, implemented, and refined over a 3-year period. This article describes the process improvement and subsequent leveling of workload within data management and regulatory that enhanced the efficiency of UMCCC's clinical trials operation.

Effort tracking metrics provide data for optimal budgeting and workload management in therapeutic cancer clinical trials.

Clinical trials operations struggle to achieve optimal distribution of workload in a dynamic data management and regulatory environment, and to achieve adequate cost recovery for personnel costs. The University of Michigan Comprehensive Cancer Center developed and implemented an effort tracking application to quantify data management and regulatory workload to more effectively assess and allocate work while improving charge capture. Staff recorded how much time they spend each day performing specific study-related and general office tasks. Aggregated data on staff use of the application from 2006 through 2009 were analyzed to gain a better understanding of what trial characteristics require the most data management and regulatory effort. Analysis revealed 4 major determinants of staff effort: 1) study volume (actual accrual), 2) study accrual rate, 3) study enrollment status, and 4) study sponsor type. Effort tracking also confirms that trials that accrued at a faster rate used fewer resources on a per-patient basis than slow-accruing trials. In general, industry-sponsored trials required the most data management and regulatory support, outweighing other sponsor types. Although it is widely assumed that most data management efforts are expended while a trial is actively accruing, the authors learned that 25% to 30% of a data manager's effort is expended while the study is either not yet open or closed to enrollment. Through the use of a data-driven effort tracking tool, clinical research operations can more efficiently allocate workload and ensure that study budgets are negotiated to adequately cover study-related expenses.