Dataset Information

Aligning Strategic Interests in an Academic Medical Center: A Framework for Evaluating GME Expansion Requests.

ABSTRACT:

Background

In 2017, the Maine Medical Center Graduate Medical Education Committee received an unprecedented number of requests (n?=?18) to start new graduate medical education (GME) programs or expand existing programs. There was no process by which multiple programs could be prioritized to compete for scarce GME resources.

Objective

We developed a framework to strategically assess and prioritize GME program expansion requests to yield the greatest benefits for patients, learners, and the institution as well as to meet regional and societal priorities.

Methods

A systems engineering methodology called tradespace exploration was applied to a 6-step process to identify relevant categories and metrics. Programs' final scores were peer evaluated, and prioritization recommendations were made. Correlation analysis was used to evaluate the relevance of each category to final scores. Stakeholder feedback was solicited for process refinement.

Results

Five categories relevant to GME expansion were identified: institutional priorities, health care system priorities, regional and societal needs, program quality, and financial considerations. All categories, except program quality, correlated well with final scores (R ² range 0.413-0.662). Three of 18 requested programs were recommended for funding. A stakeholder survey revealed that almost half of respondents (48%, 14 of 29) agreed that the process was unbiased and inclusive. Focus group feedback noted that the process had been rigorous and deliberate, although communication could have been improved.

Conclusions

Applying a systems engineering approach to develop institution-specific metrics for assessing GME expansion requests provided a reproducible framework, allowing consideration of institutional, health care system, and regional societal needs, as well as program quality and funding considerations.

SUBMITTER: Varaklis K

PROVIDER: S-EPMC6375329 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Aligning Strategic Interests in an Academic Medical Center: A Framework for Evaluating GME Expansion Requests.

Varaklis Kalli K Parker Mark G MG Peck Jordan S JS Bing-You Robert G RG

Journal of graduate medical education 20190201 1

<h4>Background</h4>In 2017, the Maine Medical Center Graduate Medical Education Committee received an unprecedented number of requests (n = 18) to start new graduate medical education (GME) programs or expand existing programs. There was no process by which multiple programs could be prioritized to compete for scarce GME resources.<h4>Objective</h4>We developed a framework to strategically assess and prioritize GME program expansion requests to yield the greatest benefits for patients, learners, ...[more]

PMID: 30805103

Similar Datasets

Project description:BackgroundEthics consult services are well established, but often remain underutilized. Our aim was to identify the barriers and perceptions of the Ethics consult service for physicians, advance practice providers (APPs), and nurses at our urban academic medical center which might contribute to underutilization.MethodsThis was a cross-sectional single-health system, anonymous written online survey, which was developed by the UCSD Health Clinical Ethics Committee and distributed by Survey Monkey. We compare responses between physicians, APPs, and nurses using standard parametric and non-parametric statistical methods. Satisfaction with ethics consult and likelihood of calling Ethics service again were assessed using a 0-100 scale using a 5-likert response structured (0 being "not helpful at all" to 100 being "extremely helpful") and results presented using box plots and interquartile ranges (IQR).ResultsFrom January to July 2019, approximately 3800 surveys were sent to all physicians, APPs and nurses with a return rate of 5.5-10%. Although the majority of respondents had encountered an ethical dilemma (85-92.1%) only approximately half had ever requested an Ethics consult. The primary reason for physicians never having requested a consult was that they never felt the need for help (41%). For APPs the primary reasons were not knowing an Ethics consult service was available (33.3%) or not knowing how to contact Ethics (27.8%). For nurses, it was not knowing how to contact the Ethics consult service (30.8%) or not feeling the need for help (26.2%). The median satisfaction score (IQR) for Ethics consult services rated on a 0-100 scale, from physicians was 76 (29), for AAPs 89 (49), and nurses 70 (40) (p = 0.62). The median (IQR) of likelihood of consulting Ethics in the future also on a 0-100 scale was 71 (47) for physicians, 69 (45) for APPs, and 61 (45) for nurses (p = 0.79). APP's and nurses were significantly more likely than physicians to believe that the team did not act on the Ethics consult's recommendations.ConclusionsBased on the results presented, we were able to identify actionable steps to better engage healthcare providers-and in particular APPs and nurses-and scale up institutional educational efforts to increase awareness of the role of the Ethics consult service at our institution. Actionable steps included implementing a system of ongoing feedback that is critical for the sustainability of the Ethics service role. We hope this project can serve as a blueprint for other hospital-based Ethics consult services to improve the quality of their programs.

Project description:BackgroundOver the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative methods vs. generative methods, and so on. In some of the more popular comparative methods, researchers compare observed epidemiological data from the early stages of an outbreak with the output of proposed models to forecast the future trend and prevalence of the pandemic. A significant problem in this area is the lack of standard well-defined evaluation measures to select the best algorithm among different ones, as well as for selecting the best possible configuration for a particular algorithm.ResultsIn this paper we present an evaluation framework which allows for combining different features, error measures, and ranking schema to evaluate forecasts. We describe the various epidemic features (Epi-features) included to characterize the output of forecasting methods and provide suitable error measures that could be used to evaluate the accuracy of the methods with respect to these Epi-features. We focus on long-term predictions rather than short-term forecasting and demonstrate the utility of the framework by evaluating six forecasting methods for predicting influenza in the United States. Our results demonstrate that different error measures lead to different rankings even for a single Epi-feature. Further, our experimental analyses show that no single method dominates the rest in predicting all Epi-features when evaluated across error measures. As an alternative, we provide various Consensus Ranking schema that summarize individual rankings, thus accounting for different error measures. Since each Epi-feature presents a different aspect of the epidemic, multiple methods need to be combined to provide a comprehensive forecast. Thus we call for a more nuanced approach while evaluating epidemic forecasts and we believe that a comprehensive evaluation framework, as presented in this paper, will add value to the computational epidemiology community.

Dataset Information

Aligning Strategic Interests in an Academic Medical Center: A Framework for Evaluating GME Expansion Requests.

Background

Objective

Methods

Results

Conclusions

Publications

Aligning Strategic Interests in an Academic Medical Center: A Framework for Evaluating GME Expansion Requests.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure