Abstract
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.
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References
Antzoulakos D, Rakitzis A (2008) The modified r out of m control chart. Commun Stat Simul Comput 37(2):396–408
Balakrishnan N, Bersimis S, Koutras MV (2009) Run and frequency quota rules in process monitoring and acceptance sampling. J Qual Technol 41(1):66–81
Bersimis S, Sachlas A, Papaioannou T (2015) Flexible designs for phase ii comparative clinical trials involving two response variables. Stat Med 34(2):197–214
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):67–73
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 Raton
Grigg O, Farewell V (2004) An overview of risk-adjusted charts. J R Stat Soc Ser A 167:523–539
Koutras MV, Bersimis S, Antzoulakos DL (2006) Improving the performance of the chi-square control chart via runs rules. Methodol Comput Appl Probab 8(3):409–426
Koutras MV, Bersimis S, Maravelakis PE (2007) Statistical process control using shewhart control charts with supplementary runs rules. Methodol Comput Appl Probab 9(2):207–224
Lim TO, Soraya A, Ding LM, Morad Z (2002) Assessing doctors’ competence: application of cusum technique in monitoring doctors’ performance. Int J Qual Health Care 14(3):251–258
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
Montgomery DC (2013) Introduction to Statistical Quality Control, 7th edn. Wiley, New Jersey
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):168–174
Reynolds MR, Stoumbos ZG (2000) A general approach to modeling cusum charts for a proportion. IIE Trans 32:515–535
Reynolds MR, Stoumbos ZG (2004) Should observations be grouped for elective process monitoring? J Qual Technol 36(4):343–366
Ryan AG, Wells LJ, Woodall WH (2011) Methods for monitoring multiple proportions when inspecting continuously. J Qual Technol 43:237–248
Ryan SA, Thompson CB (2002) The use of aggregate data for measuring practice improvement. Semin Nurse Manag 10(2):90–94
Schuh A, Woodall WH, Camelio JA (2013) The effect of aggregating data when monitoring a poisson process. J Qual Technol 45(3):260–272
Sego LH, Reynolds MR Jr, Woodall WH (2009) Risk-adjusted monitoring of survival times. Stat Med 28:1386–1401
Steiner SH, Cook RJ, Farewell VT, Treasure T (2000) Monitoring surgical performance using risk-adjusted cumulative sum charts. Biostatistics 1:441–452
Topalidou E, Psarakis S (2009) Review of multinomial and multi-attribute quality control charts. Qual Reliab Eng Int 25(7):773–804
Tsui KL, Chiu W, Gierlich P, Goldsman D, Liu X, Maschek T (2008) A review of healthcare, public health, and syndromic surveillance. Qual Eng 20:435–450
Woodall WH (1997) Control charting based on attribute data: Bibliography and review. J Qual Technol 29(2):172–183
Woodall WH, Montgomery DC (2004) Some current directions in the theory and application of statistical process monitoring. J Qual Technol 46(1):78–93
Zhang M, Megahed FM, Woodall WH (2014) Exponential cusum charts with estimated control limits. Qual Reliab Eng 30:275–286
Zhang X, Woodall WH (2015) Dynamic probability control limits for risk-adjusted bernoulli cusum charts, Stat Med. doi:10.1002/sim.6547
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The authors are supported by the General Secretariat for Research and Technology (GSRT, Ministry of Education, Greece) research funding action “ARISTEIA II”.
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Bersimis, S., Sachlas, A. & Sparks, R. Performance Monitoring and Competence Assessment in Health Services. Methodol Comput Appl Probab 19, 1169–1190 (2017). https://doi.org/10.1007/s11009-017-9563-6
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DOI: https://doi.org/10.1007/s11009-017-9563-6
Keywords
- Competence assessment
- Control charting
- Health services monitoring
- Markov Chain embedding technique
- Multi-attribute comparative processes
- Process monitoring
- Testing competence