Objective measures of workload in healthcare: a narrative review.

Autor:	Fishbein D; Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania, USA., Nambiar S; Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, USA., McKenzie K; Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, USA., Mayorga M; Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, USA., Miller K; National Center for Human Factors in Healthcare, MedStar Health, Washington, District of Columbia, USA., Tran K; LeBow College of Business, Drexel University, Philadelphia, Pennsylvania, USA., Schubel L; National Center for Human Factors in Healthcare, MedStar Health, Washington, District of Columbia, USA., Agor J; School of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA., Kim T; National Center for Human Factors in Healthcare, MedStar Health, Washington, District of Columbia, USA., Capan M; LeBow College of Business, Drexel University, Philadelphia, Pennsylvania, USA.
Jazyk:	angličtina
Zdroj:	International journal of health care quality assurance [Int J Health Care Qual Assur] 2019 Dec 20; Vol. 33 (1), pp. 1-17.
DOI:	10.1108/IJHCQA-12-2018-0288
Abstrakt:	Purpose: Workload is a critical concept in the evaluation of performance and quality in healthcare systems, but its definition relies on the perspective (e.g. individual clinician-level vs unit-level workload) and type of available metrics (e.g. objective vs subjective measures). The purpose of this paper is to provide an overview of objective measures of workload associated with direct care delivery in tertiary healthcare settings, with a focus on measures that can be obtained from electronic records to inform operationalization of workload measurement. Design/methodology/approach: Relevant papers published between January 2008 and July 2018 were identified through a search in Pubmed and Compendex databases using the Sample, Phenomenon of Interest, Design, Evaluation, Research Type framework. Identified measures were classified into four levels of workload: task, patient, clinician and unit. Findings: Of 30 papers reviewed, 9 used task-level metrics, 14 used patient-level metrics, 7 used clinician-level metrics and 20 used unit-level metrics. Key objective measures of workload include: patient turnover ( n =9), volume of patients ( n =6), acuity ( n =6), nurse-to-patient ratios ( n =5) and direct care time ( n =5). Several methods for operationalization of these metrics into measurement tools were identified. Originality/value: This review highlights the key objective workload measures available in electronic records that can be utilized to develop an operational approach for quantifying workload. Insights gained from this review can inform the design of processes to track workload and mitigate the effects of increased workload on patient outcomes and clinician performance. (© Emerald Publishing Limited.)
Databáze:	MEDLINE
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