Abstract
Nursing homes face ever-tightening healthcare budgets and are searching for ways to increase the efficiency of their healthcare processes without losing sight of the needs of their residents. Optimizing the allocation of care workers plays a key role in this search as care workers are responsible for the daily care of the residents and account for a significant proportion of the total labor expenses. In practice, the lack of reliable data makes it difficult for nursing home managers to make informed staffing decisions. The focus of this study lies on the ‘care on demand’ process in a Belgian nursing home. Based on the analysis of real-life ‘call button’ data, a queueing model is presented which can be used by nursing home managers to determine the number of care workers required to meet a specific service level. Based on numerical experiments an 80/10 service level is proposed for this nursing home, meaning that at least 80 percent of the clients should receive care within 10 minutes after a call button request. To the best of our knowledge, this is the first attempt to develop a quantitative model for the ‘care on demand’ process in a nursing home.
Similar content being viewed by others
Notes
From now on we use the term ‘healthcare activities’ instead of ‘(health)care and support activities’.
The term ‘night’ refers to the time period between 22:45 and 5:45. This is the largest possible range in which the average number of arrivals per quarter does not exceed 2.2.
References
Abate J, Choudhury GL, Whitt W (1995) Exponential approximations for tail probabilities in queues, I: waiting times. Oper Res 43(5):885–901
Bosman R, Bours G, Engels J, De Wit P (2008) Client-centred care perceived by clients of two Dutch homecare agencies: A questionary survey. Int J Nurs Stud 45(5):518–525
Brazil K, Maitland J, Ploeg J, Denton M (2012) Identifying research priorities in long term care homes. J Am Med Dir Assoc 13(1):84–e1
De Bruin AM, Bekker R, Van Zanten L, Koole GM (2010) Dimensioning hospital wards using the Erlang loss model. Ann Oper Res 178(1):23–43
Cochran JK, Bharti A (2006) Stochastic bed balancing of an obstetrics hospital. Health Care Manag Sci 9(1):31–45
Colombo F, Nozal AL, Mercier J, Tjadens F (2012) Help Wanted? Providing and Paying for Long-Term Care. OECD Health Policy Studies. OECD publishing
European Commission (2008) Long-Term Care and Use an Supply in Europe. European Union
De Véricourt F, Jennings OB (2011) Nurse staffing in medical units: A queueing perspective. Oper Res 59(6):1320–1331
El-Darzi E, Vasilakis C, Chaussalet T, Millard PH (1998) A simulation modelling approach to evaluating length of stay, occupancy, emptiness and bed blocking in a hospital geriatric department. Health Care Manag Sci 1(2):143–149
Fomundam S, Herrmann J (2007) A survey of queuing theory applications in health care. ISR Tech Rep:24
Fujisawa R, Colombo F (2009) The long-term care workforce: overview and strategies to adapt supply to a growing demand. OECD publishing
Geerts J, Willem P, Mot E (2012) Long-Term Care Use and Supply in Europe: Projections for Germany, The Netherlands, Spain and Poland, ENEPRI
Gorunescu F, McClean SI, Millard PH (2002) Using a queueing model to help plan bed allocation in a department of geriatric medicine. Health Care Manag Sci 5(4):307–312
Green LV, Kolesar PJ, Soares J (2001) Improving the SIPP approach for staffing service systems that have cyclic demands. Oper Res 49(4):549–564
Green LV, Kolesar PJ, Whitt W (2007) Coping with time-varying demand when setting staffing requirements for a service system. Prod Oper Manag 16(1):13–39
Hall R (2012) Handbook of Healthcare System Scheduling. International Series in Operations Research & Management Science. Springer
Harrington C, Choiniere J, Goldmann M, Jacobsen FF, Lloyds L, McGregor M, Stamatopoulos V, Szebehely M (2012) Nursing home staffing standards and staffing levels in six countries. J Nurs Scholarsh 44(1):88–98
Havig K, Skogstad A, Kjekhus LE, Romoren TI (2011) Leadership, staffing and quality of care in nursing homes. BMC Health Serv Res 11(1):327
Hulshof PJH, Kortbeek N, Boucherie RJ, Hans E W, Bakker PJM (2012) Taxonomic classification of planning decisions in health care: a structured review of the state of the art in OR/MS. Health Syst 1(2):129–175
Kimura T (1994) Approximations for multi-server queues: system interpolations. Queueing Syst 17(3-4):347–382
Kleinrock L (1975) Queueing systems: Theory, 1
Lakshmi C, Appa lyer S (2013) Application of queueing theory in health care: A literature review. Oper Res Health Care 2(1):25–39
McGilton KS, Tourangeau A, Kavcic C, Wodchis WP (2013) Determinants of regulated nurses’ intention to stay in long-term care homes. J Nurs Manag 21(5):771–781
Moeke D, Koole GM, Verkooijen HEC (2014) Scale and skill-mix efficiencies in nursing home staffing. Health Syst 3(1):18–28
Moeke D, Verkooijen HEC (2013) Doing more with less: A client-centred approach to healthcare logistics in a nursing home setting. J Soc Interv: Theory Pract 22(2):167-187
United Nations (2013) World population ageing 2013
Neely A, Gregory M, Platts K (2005) Performance measurement system design: A literature review and research agenda. Int J Oper Prod Manag 25(12):1228–1263
Reitinger E, Froggatt K, Brazil K, Heimerl K, Hockley J, Kunz R, Morbey H, Parker D, Husebo BS (2013) Palliative care in long-term care settings for older people: findings from an EAPC taskforce. Eur J Palliat Care 20(5):251–253
Sakasegawa H (1977) An approximation formula L q ≃ α⋅ρ β/(1−ρ). Ann Inst Stat Math 29(1):67–75
Satyam K, Krishnamurthy A, Kamath M (2013) Solving general multi-class closed queuing networks using parametric decomposition. Comput Oper Res 40(7):1777–1789
Spilsbury K, Hewitt C, Stirk L, Bowman C (2011) The relationship between nurse staffing and quality of care in nursing homes: A systematic review. Int J Nurs Stud 48(6):732–750
Storey JD (2002) A direct approach to false discovery rates. J R Stat Soc, Ser B 64(3):479–498
Storey JD (2003) The positive false discovery rate: A Bayesian interpretation and the q-value. Ann Stat 31(6):2013–2035
Tijms HC (2003) A First Course in Stochastic Models. Wiley
Van den Akker M, Buntinx F, Metsemakers JFM, Roos S, Knottnerus JA (1998) Multimorbidity in general practice: Prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases. J Clin Epidemiol 51(5):367–375
Whitt W (1984) Open and closed models for networks of queues. AT T Bell Lab Tech J 63(9):1911–1979
Whitt W (1992) Understanding the efficiency of multi-server service systems. Manag Sci 38(5)
Whitt W (1993) Approximations for the GI/G/m queue. Prod Oper Manag 2(2):114–161
Yankovic N, Green LV (2011) Identifying good nursing levels: A queuing approach. Oper Res 59(4):942–955
Acknowledgments
The authors would like to thank Niko Projects for providing us with a dataset and Jan-Pieter Dorsman for the simulation of the finite-source queueing model.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
van Eeden, K., Moeke, D. & Bekker, R. Care on demand in nursing homes: a queueing theoretic approach. Health Care Manag Sci 19, 227–240 (2016). https://doi.org/10.1007/s10729-014-9314-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10729-014-9314-y