Dataset Information

Data Day to Day: building a community of expertise to address data skills gaps in an academic medical center.

ABSTRACT:

Background

The New York University Health Sciences Library data services team had developed educational material for research data management and data visualization and had been offering classes at the request of departments, research groups, and training programs, but many members of the medical center were unaware of these library data services. There were also indications of data skills gaps in these subject areas and other data-related topics.

Case presentation

The data services team enlisted instructors from across the medical center with data expertise to teach in a series of classes hosted by the library. We hosted eight classes branded as a series called "Data Day to Day." Seven instructors from four units in the medical center, including the library, taught the classes. A multipronged outreach approach resulted in high turnout. Evaluations indicated that attendees were very satisfied with the instruction, would use the skills learned, and were interested in future classes.

Conclusions

Data Day to Day met previously unaddressed data skills gaps. Collaborating with outside instructors allowed the library to serve as a hub for a broad range of data instruction and to raise awareness of library services. We plan to offer the series three times in the coming year with an expanding roster of classes.

SUBMITTER: Surkis A

PROVIDER: S-EPMC5370612 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Publications

Data Day to Day: building a community of expertise to address data skills gaps in an academic medical center.

Surkis Alisa A LaPolla Fred Willie Zametkin FW Contaxis Nicole N Read Kevin B KB

Journal of the Medical Library Association : JMLA 20170401 2

<h4>Background</h4>The New York University Health Sciences Library data services team had developed educational material for research data management and data visualization and had been offering classes at the request of departments, research groups, and training programs, but many members of the medical center were unaware of these library data services. There were also indications of data skills gaps in these subject areas and other data-related topics.<h4>Case presentation</h4>The data servic ...[more]

PMID: 28377684

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Project description:BackgroundAcademic Medical Centers (AMCs) must simultaneously serve different purposes: Delivery of high quality healthcare services to patients, as the main mission, supported by other core missions such as academic activities, i.e., researching, teaching and tutoring, while maintaining solvency. This study aims to develop a methodology for constructing models evaluating the academic value provided by AMCs and implementing it at the largest AMC in Israel.MethodsThirty five practiced educators and researchers, academic experts, faculty members and executives, all employed by a metropolitan 1500-bed AMC, were involved in developing academic quality indicators. First, an initial list of AMCs' academic quality indicators was drafted, using a literature review and consulting scholars. Afterwards, additional data and preferences were collected by conducting semi-structured interviews, complemented by a three-round Delphi Panel. Finally, the methodology for constructing a model evaluating the academic value provided by the AMC was developed.ResultsThe composite academic quality indicators methodology consists of nine indicators (relative weight in parentheses): 'Scientific Publications Value' (18.7%), 'Completed Studies' (13.5%), 'Authors Value' (13.0%), 'Residents Quality' (11.3%), 'Competitive Grants Budget' (10.2%), 'Academic Training' (8.7%), 'Academic Positions' (8.3%), 'Number of Studies' (8.3%) and 'Academic Supervision' (8.0%). These indicators were grouped into three core categories: 'Education', 'Research' and 'Publications', having almost the same importance on a scale from zero to one (0-1), i.e., 0.363, 0.320, and 0.317, respectively. The results demonstrated a high level of internal consistency (Cronbach-alpha range: 0.79-0.86).ConclusionsWe have found a gap in the ability to measure academic value provided by AMCs. The main contribution of this research is the development of methodology for constructing evaluation models for AMCs academic performance. Further studies are needed to further test the validity and reliability of the proposed methodology at other sites.

Project description:BACKGROUND:The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey is the first publicly reported nationwide survey to evaluate and compare hospitals. Increasing patient satisfaction is an important goal as it aims to achieve a more effective and efficient healthcare delivery system. In this study, we develop and apply an integrative, data-driven approach to identify clinical risk factors that associate with patient satisfaction outcomes. METHODS:We included 1,771 unique adult patients who completed the HCAHPS survey and were discharged from the inpatient Medicine service from 2010 to 2012. We collected 266 clinical features including patient demographics, lab measurements, medications, disease categories, and procedures. We developed and applied a data-driven approach to identify risk factors that associate with patient satisfaction outcomes. FINDINGS:We identify 102 significant risk factors associating with 18 surveyed questions. The most significantly recurrent clinical risk factors were: self-evaluation of health, education level, Asian, White, treatment in BMT oncology division, being prescribed a new medication. Patients who were prescribed pregabalin were less satisfied particularly in relation to communication with nurses and pain management. Explanation of medication usage was associated with communication with nurses (q = 0.001); however, explanation of medication side effects was associated with communication with doctors (q = 0.003). Overall hospital rating was associated with hospital environment, communication with doctors, and communication about medicines. However, patient likelihood to recommend hospital was associated with hospital environment, communication about medicines, pain management, and communication with nurse. CONCLUSIONS:Our study identified a number of putatively novel clinical risk factors for patient satisfaction that suggest new opportunities to better understand and manage patient satisfaction. Hospitals can use a data-driven approach to identify clinical risk factors for poor patient satisfaction to support development of specific interventions to improve patients' experience of care.

Dataset Information

Data Day to Day: building a community of expertise to address data skills gaps in an academic medical center.

Background

Case presentation

Conclusions

Publications

Data Day to Day: building a community of expertise to address data skills gaps in an academic medical center.

Similar Datasets

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