A patient stochastic model to support human resource planning in home care

A large number of variables and unpredictable events affect the quality of home care (HC) services. Changes in patient clinical and social conditions and troubles in service organisation are only some examples that can make the management of HC activities quite difficult. The estimation of patient requirements would support HC providers in human resource planning before the care execution, thus improving the service efficiency. This article proposes a stochastic model to represent the patient's care pathway; on the basis of historical data of an HC structure, the model provides predictions on the major variables of interest: how many patients are followed up in the course of time and, for each of them, the duration of care and the amount of required visits. The predicted variables of interest provide information about the future workload of each operator. This becomes a useful support tool for human resource planning in the medium and short terms. Numerical results prove the applicability of the proposed stochastic model in practice.

[1]  Zhehui Luo,et al.  A dynamic model for estimating changes in health status and costs , 2006, Statistics in medicine.

[2]  Andrea Matta,et al.  Operations management related activities in home health care structures , 2006 .

[3]  P H Millard,et al.  Modelling Patient Duration of Stay to Facilitate Resource Management of Geriatric Hospitals , 2002, Health care management science.

[4]  GORDON TAYLOR,et al.  Geriatric-patient flowrate modelling , 2005 .

[5]  Davide Verotta,et al.  Models and estimation methods for clinical HIV-1 data , 2005 .

[6]  Oguzhan Alagoz,et al.  Incorporating Biological Natural History in Simulation Models: Empirical Estimates of the Progression of End-Stage Liver Disease , 2005, Medical decision making : an international journal of the Society for Medical Decision Making.

[7]  D. Pauler,et al.  Predicting time to prostate cancer recurrence based on joint models for non‐linear longitudinal biomarkers and event time outcomes , 2002, Statistics in medicine.

[8]  D Costagliola,et al.  Markov modelling of changes in HIV‐specific cytotoxic T‐lymphocyte responses with time in untreated HIV‐1 infected patients , 2003, Statistics in medicine.

[9]  J B Wong,et al.  Estimates of the Cost-Effectiveness of a Single Course of Interferon-2b in Patients with Histologically Mild Chronic Hepatitis C , 1997, Annals of Internal Medicine.

[10]  P Congdon,et al.  The Development of Gravity Models for Hospital Patient Flows under System Change: A Bayesian Modelling Approach , 2001, Health care management science.

[11]  Peter H. Millard,et al.  Where to treat the older patient? Can Markov models help us better understand the relationship between hospital and community care? , 2007, J. Oper. Res. Soc..

[12]  Giulia Asquer,et al.  Analisi della struttura organizzativa degli erogatori del servizio di assistenza domiciliare , 2007 .

[13]  Maria Cristina Saetti,et al.  Time ordering in frontal lobe patients: A stochastic model approach , 2005, Brain and Cognition.

[14]  Murray Krahn,et al.  Estimating the Prognosis of Hepatitis C Patients Infected by Transfusion in Canada between 1986 and 1990 , 2004, Medical decision making : an international journal of the Society for Medical Decision Making.

[15]  Steven Walczak,et al.  Nurse Scheduling: From Academia to Implementation or Not? , 2007, Interfaces.

[16]  P H Millard,et al.  A three compartment model of the patient flows in a geriatric department: a decision support approach , 1998, Health care management science.

[17]  Michael J. Brusco,et al.  Cross validation issues in multiobjective clustering. , 2009, The British journal of mathematical and statistical psychology.

[18]  Adele H Marshall,et al.  Using Coxian Phase-Type Distributions to Identify Patient Characteristics for Duration of Stay in Hospital , 2004, Health care management science.

[19]  Brian D Tom,et al.  Description and prediction of physical functional disability in psoriatic arthritis: a longitudinal analysis using a Markov model approach. , 2005, Arthritis and rheumatism.

[20]  S McClean,et al.  Geriatric-patient flow-rate modelling. , 1996, IMA journal of mathematics applied in medicine and biology.

[21]  A. Matta,et al.  A Home Care Scheduling Model For Human Resources , 2006, 2006 International Conference on Service Systems and Service Management.

[22]  Carlo Berzuini,et al.  Effectiveness of potent antiretroviral therapy on progression of human immunodeficiency virus: Bayesian modelling and model checking via counterfactual replicates , 2004 .

[23]  S. Katz,et al.  Progress in development of the index of ADL. , 1970, The Gerontologist.

[24]  Marvin Zelen,et al.  Mortality Modeling of Early Detection Programs , 2008, Biometrics.

[25]  Sally McClean,et al.  CONTINUOUS-TIME MARKOV MODELS FOR GERIATRIC PATIENT BEHAVIOUR , 1997 .

[26]  L. Laliberte,et al.  The Karnofsky performance status scale: An examination of its reliability and validity in a research setting , 1984, Cancer.

[27]  Elia El-Darzi,et al.  Length of Stay-Based Patient Flow Models: Recent Developments and Future Directions , 2005, Health care management science.

[28]  N Senninger,et al.  Cost-effectiveness analysis of basixilimab induction and calcineurin-sparing protocols in "old to old" programs using Markov models. , 2003, Transplantation proceedings.

[29]  Marvin Zelen,et al.  Early detection of disease and scheduling of screening examinations , 2004, Statistical methods in medical research.

[30]  A. Petkau,et al.  Application of hidden Markov models to multiple sclerosis lesion count data , 2005, Statistics in medicine.

[31]  A. Verbeek,et al.  Routine follow-up examinations in breast cancer patients have minimal impact on life expectancy: a simulation study. , 2001, Annals of oncology : official journal of the European Society for Medical Oncology.

[32]  J. Dale,et al.  Paediatric home care for acute illness: I. GPs’ and hospital‐at‐home staff views , 2003 .

[33]  J. Hutton,et al.  A Markov model of treatment of newly diagnosed epilepsy in the UK , 2003, The European Journal of Health Economics, formerly: HEPAC.

[34]  C. Jagger,et al.  Economic evaluation of hospital at home versus hospital care: cost minimisation analysis of data from randomised controlled trial , 1999, BMJ.

[35]  T. Tolio,et al.  A Multi Agent Architecture for Home Care Services , 2006 .

[36]  R. Bergamaschi,et al.  Usefulness of Bayesian graphical models for early prediction of disease progression in multiple sclerosis , 2000, Neurological Sciences.

[37]  S. Flessa Decision support for malaria‐control programmes – a system dynamics model , 1999, Health care management science.

[38]  N. Koizumi,et al.  Modeling Patient Flows Using a Queuing Network with Blocking , 2005, Health Care Management Science.

[39]  M. Lawton,et al.  Assessment of older people: self-maintaining and instrumental activities of daily living. , 1969, The Gerontologist.

[40]  Sally McClean,et al.  Using a Markov reward model to estimate spend-down costs for a geriatric department , 1998, J. Oper. Res. Soc..

[41]  Sally McClean,et al.  Stochastic models of geriatric patient bed occupancy behaviour , 2000 .