Performance Monitoring and Competence Assessment in Health Services
- 87 Downloads
Health and health service monitoring is among the most promising research area today and the world work towards efficient and cost effective health care. This paper deals with monitoring health service performance using more than one performance outcome variable (multi-attribute processes), which is common in most health services. Although monitoring whether a health service changes or improves over time is important this is well covered in the current literature. Therefore this paper focuses on comparing similar health services in terms of their performance. The proposed procedure is based on an appropriate control chart. The paper deals with firstly the case when no risk adjustment is required because the health services being compared treat the same patient case-mix which does not vary over time. Secondly it deals with comparing health services where risk adjustment is required because the patient case-mix they service do differ because they service either very different geographical locations or service very different demographics of the same population. The technology developed in this paper could be used for example to assess and compare health practitioners’ competence over time, i.e. to decide if two doctors are equivalent in terms of their outcome performances. The waiting time random variable associated with the run length distribution of the control charts (as well as to competence testing) is studied using a Markov Chain embedding technique. Numerical results are provided that exhibit the value of the proposed procedures.
KeywordsCompetence assessment Control charting Health services monitoring Markov Chain embedding technique Multi-attribute comparative processes Process monitoring Testing competence
Mathematics Subject Classification (2010)60J20 62P10
Unable to display preview. Download preview PDF.
- Burkom HS, Elbert Y, Feldman A, Lin J (2004) Role of data aggregation in biosurveillance detection strategies with applications from essence. Morb Mortal Wkly Rep 53(Suppl):67–73Google Scholar
- Dubrawski A (2011) The role of data aggregation in public health and food safety surveillance. In: Kass-Hout T, Zhang X (eds) Biosurveillance - Methods and Case Studies. FL: CRC/Taylor & Francis Group, Boca RatonGoogle Scholar
- Maruthappu M, Carty MJ, Lipsitz SR, Wright J, Orgill D, Duclos A (2014) Patient- and surgeon-adjusted control charts for monitoring performance. BMJ Open 4:e004046. doi: 10.1136/bmjopen-2013-004046
- Rethans JJ, Van Leeuwen Y, Drop R, Vand Der Vleuten C, Sturmans F (1990) Competence and performance: two different concepts in the assessment of quality of medical care. Fam Pract 7(3):168–174Google Scholar
- Reynolds MR, Stoumbos ZG (2000) A general approach to modeling cusum charts for a proportion. IIE Trans 32:515–535Google Scholar
- Reynolds MR, Stoumbos ZG (2004) Should observations be grouped for elective process monitoring? J Qual Technol 36(4):343–366Google Scholar
- Ryan SA, Thompson CB (2002) The use of aggregate data for measuring practice improvement. Semin Nurse Manag 10(2):90–94Google Scholar
- Woodall WH (1997) Control charting based on attribute data: Bibliography and review. J Qual Technol 29(2):172–183Google Scholar
- Zhang X, Woodall WH (2015) Dynamic probability control limits for risk-adjusted bernoulli cusum charts, Stat Med. doi: 10.1002/sim.6547