Hostname: page-component-848d4c4894-m9kch Total loading time: 0 Render date: 2024-05-19T05:56:02.727Z Has data issue: false hasContentIssue false

The patient encounter index: a novel method of measuring clinical workload in a paediatric cardiology service

Published online by Cambridge University Press:  07 January 2020

Michael J. Harrison*
Affiliation:
Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
Oliver M. Barry
Affiliation:
Department of Cardiology, Boston Children’s Hospital, Boston, USA
Rachel A. Hounsell
Affiliation:
Nuffield Department of Medicine, University of Oxford, Oxford, UK
Rik De Decker
Affiliation:
Department of Paediatrics and Child Health, Red Cross War Memorial Children’s Hospital, Cape Town, South Africa
*
Author for correspondence: M. J. Harrison, MBChB, Department of Paediatrics and Child Health, Division of Cardiology, Red Cross War Memorial Children’s Hospital, Klipfontein Road, Rondebosch, Cape Town, 7700, South Africa. Tel: +27 78 120 3604; Fax: +27 21 650 7391. E-mail: michael.john.thomas.harrison@gmail.com

Abstract

Technological advances have led to better patient outcomes and the expansion of clinical services in paediatric cardiology. This expansion creates an ever-growing workload for clinicians, which has led to workflow and staffing issues that need to be addressed. The objective of this study was the development of a novel tool to measure the clinical workload of a paediatric cardiology service in Cape Town, South Africa: The patient encounter index is a tool designed to quantify clinical workload. It is defined as a ratio of the measured duration of clinical work to the total time available for such work. This index was implemented as part of a prospective cross-sectional study design. Clinical workload data were collected over a 10-day period using time-and-motion sampling. Clinicians were contractually expected to spend 50% of their daily workload on patient care. The median patient encounter index for the Western Cape Paediatric Cardiac Service was 0.81 (range 0.19–1.09), reflecting that 81% of total contractual working time was spent on clinical activities. This study describes the development and implementation of a novel tool for clinical workload quantification and describes its application to a busy paediatric cardiology service in Cape Town, South Africa. This tool prospectively quantifies clinical workload which may directly influence patient outcomes. Implementation of this novel tool in the described setting clearly demonstrated the excessive workload of the clinical service and facilitated effective motivation for improved allocation of resources.

Type
Original Article
Copyright
© Cambridge University Press 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Dowie, R, Mistry, H, Young, TA, et al. Telemedicine in pediatric and perinatal cardiology: economic evaluation of a service in English hospitals. Int J Technol Assess Health Care 2007; 23: 116125.CrossRefGoogle ScholarPubMed
Kleinman, CS. Chapter 18 – impact of prenatal diagnosis on the management of congenital heart disease. In: Kleinman, CS, Seri, I, eds. Hemodynamics and Cardiology: Neonatology Questions and Controversies. Saunders, Philadelphia, 2008: 323338.CrossRefGoogle Scholar
Dubé, MP, Lipshultz, SE, Fichtenbaum, CJ, Greenberg, R, Schecter, AD, Fisher, SD. Effects of HIV infection and antiretroviral therapy on the heart and vasculature. Circulation 2008; 118: 3641.CrossRefGoogle ScholarPubMed
Kifle, M, Mbarika, VWA, Datta, P. Interplay of cost and adoption of tele-medicine in Sub-Saharan Africa: the case of tele-cardiology in Ethiopia. Inf Syst Front 2006; 8: 211223.CrossRefGoogle Scholar
Lubega, S, Zirembuzi, G, Lwabi, P. Heart disease among children with HIV/AIDS attending the paediatric infectious disease clinic at Mulago Hospital. Afr Health Sci 2005; 5: 219226.Google ScholarPubMed
Engel, ME, Zühlke, LJ, Robertson, K. Rheumatic fever and rheumatic heart disease : where are we now in South Africa? SA Hear 2009; 6: 2023.Google Scholar
Statistics South Africa. Mid-year population estimates 2019. Pretoria, 2019.Google Scholar
Statistics South Africa. Recorded live births 2017. Pretoria, 2019.Google Scholar
Zühlke, LJ, Engel, M, Watkins, D, Mayosi, B. Incidence, prevalence and outcome of rheumatic heart disease in South Africa : a systematic review of contemporary studies. Int J Cardiol 2015; 199: 375383.CrossRefGoogle ScholarPubMed
de Cordova, PB, Lucero, RJ, Hyun, S, Quinlan, P, Price, K, Stone, P. NIH public access. J Nurs Care Qual 2010; 25: 3945.CrossRefGoogle Scholar
Hoonakker, P, Carayon, P, Gurses, AP, Brown, R, Mcguire, K, Walker, JM. Measuring workload of ICU nurses with a questionnaire survey: the NASA Task Load Index (TLX). IIE Trans Healthc Syst Eng 2011; 1: 131143.CrossRefGoogle Scholar
Finkler, SA, Knickman, JR, Hendrickson, G, Lipkin, M, Thompson, WG. A comparison of work-sampling and time-and-motion techniques for studies in health services research. Health Serv Res 1993; 28: 577597.Google ScholarPubMed
Lundgrén-Laine, H, Suominen, T. Nursing intensity and patient classification at an adult intensive care unit (ICU). Intensive Crit Care Nurs 2007; 23: 97103.CrossRefGoogle Scholar
Camuci, MB, Martins, JT, Cardeli, AAM, Robazzi, ML do CC. Nursing activities score: nursing work load in a burns intensive care unit. Rev Lat Am Enfermagem 2014; 22: 325331.CrossRefGoogle Scholar
Malstam, J, Lind, J. Therapeutic intervention scoring system: a method for measuring workload and comparing costs in the ICU. Acta Anaesthesiol Scand 1992; 36: 758763.CrossRefGoogle Scholar
Kwiecien, K, Wujtewicz, M, Medrzycka-Dabrowska, W. Selected methods of measuring workload among intensive care nursing staff. Int J Occup Med Environ Health 2012; 25: 209217.CrossRefGoogle ScholarPubMed
Hendy, KC, Liao, J, Milgram, P. Combining time and intensity effects in assessing operator information-processing load. Hum Factors 1997; 39: 3047.CrossRefGoogle ScholarPubMed