Search

This article explores age differences in preferences for current health states, which is one way to measure trade-offs between 'quantity of life' and the 'quality' of those health states. Data are from 17,707 adult outpatients visiting 46 primary care, managed care practices. Patient preferences (utility) for their current health were assessed by standard gamble and time trade-off methods. Although older primary care patients' utility measurements for their current health were lower than other patient groups, most of the difference in value measurements was attributable to differences in health. Health providers should take care to assess individual preferences from all patients regardless of age. Key Words: Utility, Quality versus quantity of life

Background: As SARS-CoV-2, the virus that causes COVID-19, spread rapidly across the United States in the spring of 2020, institutions of higher education faced numerous challenges associated with minimizing risk of exposure to COVID-19 among their students, faculty, staff, and surrounding communities. This paper describes the protocol, South Carolina (SC) Safer Together, developed by Clemson University (Clemson) to design, deploy, and evaluate multi-level communication and dissemination and implementation (D&I) strategies in line with recommendations from governmental and educational agencies to mitigate the risk of exposure to COVID-19. Safer Together was enhanced by the addition of the Google/Apple Exposure Notification app, an alternative strategy to support a recommendation of COVID-19 testing outcomes: contact tracing, isolation, and quarantine. Objective: This study aimed to (1) describe the content and intended audiences of D&I strategies used to deploy recommended COVID-19 mitigation strategies on a major college campus; (2) determine the reach, acceptability, adoption, and use of D&I strategies among target audiences among university students, faculty, and staff; and (3) characterize barriers and facilitators to the implementation and use of recommended mitigation strategies. Methods: The study team incorporated elements of the Health Belief Model, the Technology Acceptance Model, communication and social marketing models, and the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework to identify and develop appropriate constructs and specific outcomes for inclusion in our approach to evaluate the communication, dissemination and implementation processes related to deployment of Safer Together at Clemson. A parallel convergent mixed methods design was used to (1) inform implementation strategies used to launch the program and (2) evaluate program reach, acceptability, adoption, and use guided by the RE-AIM framework. Data collection tools include surveys, data analytics--tracking, and focus groups or interviews with key stakeholders (students, employees, and university leadership). Results: Rigorously studying both the dissemination and implementation of Safer Together in a national public university setting is expected to yield lessons that will be valuable at many organizational and governmental settings. On a local level, broad adoption and use of the Safer Together may help reduce COVID-19 transmission and keep the university "open." On a larger scale, lessons learned on how to influence student and employee behavior with respect to the use of a public health outbreak prevention tool including Safer Together may be applicable in future pandemic and outbreak situations. Conclusions: This study proposes a structured, theory-driven approach to evaluate dissemination and implementation strategies associated with the deployment of Safer Together in a university setting from the viewpoint of students, employees, and university leadership. Our results will inform future implementation of apps such as Safer Together at major state universities in SC. [ABSTRACT FROM AUTHOR]

HMOs may be better at reducing the level of inappropriate care given to critically ill patients who are dying. This was the conclusion of a study that evaluated potentially ineffective care (PIC) in all critically ill Medicare patients in intensive care units in California in 1994. PIC is defined as extensive provision of health care resources to a patient who dies shortly thereafter. About 5% of the patients received PIC and they used 22% of the total ICU resources in the state that year. HMO members were less likely to receive PIC compared to fee-for-service patients., Context. -- The worst outcome of critical care may not be death itself; rather, the worst may be an extended death process in which a patient's and his or her family's suffering has been prolonged by services that are ultimately impotent. We have previously used potentially ineffective care (PIC) as a proxy measure for this type of care. Objective. -- To determine if PIC is delivered less often to Medicare patients enrolled in health maintenance organizations (HMOs) than those in traditional fee-for-service health plans. Patients. -- All Medicare patients hospitalized in intensive care units in California during fiscal year 1994. Outcome. -- Potentially ineffective care was defined as the concurrence of in-hospital death or death within 100 days of hospital discharge and resource use (total hospital costs) above the 90th percentile. Methods. -- Hospital costs were adjusted for institution-specific cost-to-charge ratios and local wage indices derived from Health Care Financing Administration cost reports. A multivariate regression model adjusted PIC rates for age, sex, race, elective admission to the hospital, Charlson index diseases, the 15 most common diagnosis related groups for death by 100 days, intensive care unit size, and number of residents at the hospital. Results. -- A total of 3914 (4.8%) of 81 494 patients experienced PIC and used 21.6% of total intensive care unit resources. The occurrence of PIC was less common among HMO members (adjusted odds ratio, 0.75; 95% confidence interval, 0.65-0.87). However, HMO members were not more likely to experience in-hospital death (adjusted odds ratio, 0.99; 95% confidence interval, 0.91-1.07) and only slightly more likely to experience death by 100 days after hospital discharge (adjusted odds ratio, 1.08; 95% confidence interval, 1.01-1.15). Conclusions. -- Patients who experience PIC outcomes are not uncommon in the Medicare population, and patients experiencing this outcome consume a disproportionate amount of medical resources. Medicare beneficiaries in HMO practice settings had a lower risk of experiencing PIC outcomes after adjusting for age, sex, diagnosis, comorbid conditions, and characteristics of the treating hospital. This suggests that HMO practices may be better at limiting or avoiding injudicious use of critical care near the end of life.

A newly proposed outcome measurement called potentially ineffective care (PIC) may help doctors identify critical care patients who are unlikely to benefit from treatment. Researchers studied mortality rates and hospital expenditures for 402 patients who had been admitted to an intensive care unit between Jun 1988 and Jan 1989. These patients incurred $22 million in hospital charges during their stay in the ICU. PIC was defined as resource use in the upper 25th percentile by patients who died within 100 days of discharge from the ICU. Thirteen percent of the patients fell into this category and used 32% of the total resources. The most appropriate determination of PIC involved multiplying the APACHE-predicted mortality (APM) on the first day of admission by the APM on the 5th day. A PIC value of 0.35 or more was 98% effective in identifying patients who could benefit from treatment., Objective.--To examine the limits of the effectiveness of critical care through the study of patients for whom it was ineffective. Design.--We studied the relationship between resource use and long-term outcome (2-year follow-up) in 402 consecutively admitted critical care patients to develop a benchmark for ineffective applications of critical care. We defined an outcome called potentially ineffective care (PIC), developed and evaluated a model with an independent data set to predict PIC from a patient's response to treatment, and estimated the economic effects of limiting care after a prediction of PIC. Setting.--The combined medical and surgical intensive care unit at a 600-bed university teaching hospital. Patients.--Two groups of 402 consecutively admitted critical care patients, one from 1989, the other from 1991. Main Outcome Measures and Results.--Based on observations from a two-dimensional plot of resource use vs benefit for 402 critical care patients, PIC was defined as resource use in the upper 25th percentile and survival for less than 1 00 days after discharge. Thirteen percent of the patients fell into the PIC category and used 32% of the resources. A product of the APACHE risk estimates on days 1 and 5 of at least 0.35 predicted 37% of PIC outcomes with a specificity of 98%. In a second data set, PIC outcome prediction had a sensitivity of 43% and a specificity of 94%, and a positive predictive value of 80%. For the hospital studied, reduction of intensity of treatment after a prediction of a PIC outcome would result in a reduction of hospital charges in the range of $1.8 million to $5 million per year. Conclusion.--Patients in the PIC category consumed a large portion of the resources devoted to critical care at an academic teaching hospital. We suggest a change in focus from assessment of the quality of critical care and risk-adjusted mortality to an assessment of ineffective care based on outcome and resource use and a patient's response to treatment over time. (JAMA. 1995;274:1544-1551)

The ability to analyze human specimens is the pillar of modern-day translational research. To enhance the research availability of relevant clinical specimens, we developed the Living BioBank (LBB) solution, which allows for just-in-time capture and delivery of phenotyped surplus laboratory medicine specimens. The LBB is a system-of-systems integrating research feasibility databases in i2b2, a real-time clinical data warehouse, and an informatics system for institutional research services management (SPARC). LBB delivers deidentified clinical data and laboratory specimens. We further present an extension to our solution, the Living µBiome Bank, that allows the user to request and receive phenotyped specimen microbiome data. We discuss the details of the implementation of the LBB system and the necessary regulatory oversight for this solution. The conducted institutional focus group of translational investigators indicates an overall positive sentiment towards potential scientific results generated with the use of LBB. Reference implementation of LBB is available at https://LivingBioBank.musc.edu. [ABSTRACT FROM AUTHOR]

Abstract Background Most tools for estimating utilities use clinical trial data from general health status models, such as the 36-Item Short-Form Health Survey (SF-36). A disease-specific model may be more appropriate. The objective of this study was to apply a disease-specific utility mapping function for schizophrenia to data from a large, 1-year, open-label study of long-acting risperidone and to compare its performance with an SF-36-based utility mapping function. Methods Patients with schizophrenia or schizoaffective disorder by DSM-IV criteria received 25, 50, or 75 mg long-acting risperidone every 2 weeks for 12 months. The Positive and Negative Syndrome Scale (PANSS) and SF-36 were used to assess efficacy and health-related quality of life. Movement disorder severity was measured using the Extrapyramidal Symptom Rating Scale (ESRS); data concerning other common adverse effects (orthostatic hypotension, weight gain) were collected. Transforms were applied to estimate utilities. Results A total of 474 patients completed the study. Long-acting risperidone treatment was associated with a utility gain of 0.051 using the disease-specific function. The estimated gain using an SF-36-based mapping function was smaller: 0.0285. Estimates of gains were only weakly correlated (r = 0.2). Because of differences in scaling and variance, the requisite sample size for a randomized trial to confirm observed effects is much smaller for the disease-specific mapping function (156 versus 672 total subjects). Conclusion Application of a disease-specific mapping function was feasible. Differences in scaling and precision suggest the clinically based mapping function has greater power than the SF-36-based measure to detect differences in utility.

SPARCRequest© (Services, Pricing, & Application for Research Centers) is a web-based research management system that provides a modular and adaptable "electronic storefront" for research-related services. Developed by the South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina, it was released as open source (OS) code in 2014. The adoption of SPARCRequest© accelerated in 2016, when, to ensure responsiveness to the needs of partners, its governance also became open. This governance model enables OS partners to suggest and prioritize features for new releases. As a result, the software code has become more modularized and can be easily customized to meet the diverse needs of adopting hubs. This article describes innovative aspects of the OS governance model, including a multi-institutional committee structure to set strategic vision, make operational decisions, and develop technical solutions; a virtual roadmap that ensures transparency and aligns adopters with release-based goals; and a business process model that provides a robust voting mechanism for prioritizing new features while also enabling fast-paced bug fixes. OS software evolves best in open governance environments. OS governance has made SPARCRequest© more responsive to user needs, attracted more adopters, and increased the proportion of code contributed by adopters. [ABSTRACT FROM AUTHOR]