Monitoring of high-yield and periodical processes in health care
- PDF / 1,217,513 Bytes
- 21 Pages / 595.224 x 790.955 pts Page_size
- 102 Downloads / 194 Views
Monitoring of high-yield and periodical processes in health care Nataliya Chukhrova1 · Arne Johannssen1 Received: 6 August 2019 / Accepted: 8 July 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Statistical control charts have found valuable applications in health care, having been largely adopted from operations research in manufacturing. However, the most common types are not best-suited to monitor high-yield processes (outcomes comprising true/false fractions, ‘near-zero’) and periodical processes (characterized by sequences of single populations of finite sizes), but rather to monitor variable vital signs levels and, to a lesser degree, service performance indicators. We discuss control charts that are most suitable for fraction non-conforming measurements. We focus particularly on highyield and periodical processes, i.e. range in which out-of-control conditions are expected and should be identified. For these conditions, we discuss control charts based on the family of hypergeometric distributions, explaining and comparing their application to more traditional alternatives with two health care case studies. We demonstrate that hypergeometrictype control charts provide higher sensitivity in timely identification of changing rare event fractions and are well-suited for monitoring of periodical processes, while remaining more resistant to false alarms, versus their alternatives. Keywords CCC-chart · Periodical process · High-yield process · Health care monitoring · Medical risk management · Never events Highlights • •
•
• •
We present control charts that are well-suited for monitoring health care processes by taking into account the specific characteristics of health care outcomes On the one hand, the proposed control charts enable an efficient monitoring of high-yield processes that are prevalent in health care due to the need for monitoring “never events” (such as infection outbreaks, patient falls, contaminated needle sticks) On the other hand, they ensure adequate monitoring of periodical processes that are common in health care due to restrictions (e.g. capacities in hospitals regarding patient’s admissions, surgeries, staff) or seasonalities (e.g. unscheduled care, initial denials) We provide comprehensive comparisons of the proposed control charts to commonly implemented p- and g-charts in terms of their benefits for health care monitoring We discuss implementation and evaluation of the considered control charts and present two comprehensive case studies in health care monitoring
Arne Johannssen
[email protected] 1
University of Hamburg, 20146 Hamburg, Germany
1 Introduction 1.1 Statistical control charts in health care Statistical process control charts are well-suited to support measuring, monitoring, and improving of health care outcomes. They help to distinguish common/chance from special/assignable causes of variation as a guide for health care decision making, and processes can be enhanced to perform consistently and predictably for higher qua
Data Loading...
