Advertisement

A Data-Driven Approach to Support the Understanding and Improvement of Patients’ Journeys: A Case Study Using Electronic Health Records of an Emergency Department

      Highlights

      • Extracting patient journeys patterns factually and objectively from occurrences of processes and activities is arguably one of the most important aspects of analyzing patient journeys.
      • This study describes the use of process mining as a tool to obtain a deep understanding of executed care processes from electronic health records.
      • Process mining–based patient journey patterns are used to derive and discuss patients’ length of stay, as a crucial piece of patient experiences and of particular importance for measuring operational performance of hospitals.

      Abstract

      Objectives

      Given the increasing availability of electronic health records, it has become increasingly feasible to adopt data-driven approaches to capture a deep understanding of the patient journeys. Nevertheless, simply using data-driven techniques to depict the patient journeys without an integrated modeling and analysis approach is proving to be of little benefit for improving patients’ experiences. Indeed, a model of the journey patterns is necessary to support the improvement process.

      Methods

      We presented a 3-phase methodology that integrates a process mining–based understanding of patient journeys with a stochastic graphical modeling approach to derive and analyze the analytical expressions of some important performance indicators of an emergency department including mean and variance of patients’ length of stay (LOS).

      Results

      Analytical expressions were derived and discussed for mean and variance of LOS times and discharge and admission probabilities. LOS differed significantly depending on whether a patient was admitted to the hospital or discharged. Moreover, multiparameter sensitivity equations are obtained to identify which activities contribute the most in reducing the LOS at given operating conditions so decision makers can prioritize their improvement initiatives.

      Conclusions

      Data-driven based approaches for understanding the patient journeys coupled with appropriate modeling techniques yield a promising tool to support improving patients’ experiences. The modeling techniques should be easy to implement and not only should be capable of deriving some key performance indicators of interest but also guide decision makers in their improvement initiatives.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Value in Health
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Ben-Tovim D.I.
        • Dougherty M.L.
        • O’Connell T.J.
        • McGrath K.M.
        Patient journeys: the process of clinical redesign.
        Med J Aust. 2008; 188: S14-S17
        • Gualandi R.
        • Masella C.
        • Viglione D.
        • Tartaglini D.
        Exploring the hospital patient journey: what does the patient experience?.
        PLoS One. 2019; 14e0224899
        • Curry J.
        • Fitzgerald A.
        • Prodan A.
        • Dadich A.
        • Sloan T.
        Combining patient journey modelling and visual multi-agent computer simulation: a framework to improving knowledge translation in a healthcare environment.
        Stud Health Technol Inform. 2014; 204: 25-31
        • Carayon P.
        • Wooldridge A.R.
        Improving patient safety in the patient journey: contributions from human factors engineering.
        in: Smith A. Women in Industrial and Systems Engineering. Springer, Cham, Switzerland2022: 275-299
      1. de Vries F, Tjin E, Driessen R, Vehof H, van de Kerkhof P. Exploring patient journeys through acne healthcare: a patient perspective [published online June 30, 2021]. J Dermatol Treat. https://doi.org/10.1080/09546634.2021.1940808.

        • Carayon P.
        • Wooldridge A.
        • Hoonakker P.
        • Hundt A.S.
        • Kelly M.M.
        • SEIPS
        3.0: human-centered design of the patient journey for patient safety.
        Appl Ergon. 2020; 84103033
        • Bhattacharjee P.
        • Ray P.K.
        Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: a review and reflections.
        Comput Ind Eng. 2014; 78: 299-312
        • Koizumi N.
        • Kuno E.
        • Smith T.E.
        Modeling patient flows using a queuing network with blocking.
        Health Care Manag Sci. 2005; 8: 49-60
        • Vissers J.
        • Beech R.
        • Health
        Operations Management: Patient Flow Logistics in Health Care.
        Routledge, London, United Kingdom2005
        • Olsen C.F.
        • Bergland A.
        • Debesay J.
        • Bye A.
        • Langaas A.G.
        Patient flow or the Patient’s journey? Exploring health care providers’ experiences and understandings of implementing a care pathway to improve the quality of transitional care for older people.
        Qual Health Res. 2021; 31: 1710-1723
        • Kinsman L.
        • Rotter T.
        • James E.
        • Snow P.
        • Willis J.
        What is a clinical pathway? Development of a definition to inform the debate.
        BMC Med. 2010; 8: 31
        • Alexander G.L.
        The nurse–patient trajectory framework.
        Stud Health Technol Inform. 2007; 129: 910-914
        • Lew W.Y.
        • DeMaria A.
        The divergence between clinical guidelines and practice.
        J Am Coll Cardiol. 2013; 61: 41-43
        • Ly S.
        • Runacres F.
        • Poon P.
        Journey mapping as a novel approach to healthcare: a qualitative mixed methods study in palliative care.
        BMC Health Serv Res. 2021; 21: 915
        • Percival J.
        • McGregor C.
        An evaluation of understandability of patient journey models in mental health.
        JMIR Hum Factors. 2016; 3e20
        • Scheinker D.
        • Brandeau M.L.
        Implementing analytics projects in a hospital: successes, failures, and opportunities.
        INFORMS J Appl Anal. 2020; 50: 176-189
        • Torres Ramos L.
        Healthcare process analysis: validation and improvements of a data-based method using process mining and visual analytics. Eindhoven University of Technology.
        • Beattie M.
        • Murphy D.J.
        • Atherton I.
        • Lauder W.
        Instruments to measure patient experience of healthcare quality in hospitals: a systematic review.
        Syst Rev. 2015; 4: 97
        • Gualandi R.
        • Masella C.
        • Piredda M.
        • Ercoli M.
        • Tartaglini D.
        What does the patient have to say? Valuing the patient experience to improve the patient journey.
        BMC Health Serv Res. 2021; 21: 347
        • Ponsignon F.
        • Smart A.
        • Phillips L.
        A customer journey perspective on service delivery system design: insights from healthcare.
        Int J Qual Reliab Manag. 2018; 35: 2328-2347
        • Webster C.S.
        • Jowsey T.
        • Lu L.M.
        • et al.
        Capturing the experience of the hospital-stay journey from admission to discharge using diaries completed by patients in their own words: a qualitative study.
        BMJ Open. 2019; 9e027258
        • Nuti S.
        • De Rosis S.
        • Bonciani M.
        • Murante A.M.
        Rethinking healthcare performance evaluation systems towards the people-centredness approach: their pathways, their experience, their evaluation.
        Healthc Pap. 2017; 17: 56-64
        • Farrington C.
        • Burt J.
        • Boiko O.
        • Campbell J.
        • Roland M.
        Doctors’ engagements with patient experience surveys in primary and secondary care: a qualitative study.
        Health Expect. 2017; 20: 385-394
        • Sheard L.
        • Peacock R.
        • Marsh C.
        • Lawton R.
        What’s the problem with patient experience feedback? A macro and micro understanding, based on findings from a three-site UK qualitative study.
        Health Expect. 2019; 22: 46-53
        • Boiko O.
        • Campbell J.L.
        • Elmore N.
        • Davey A.F.
        • Roland M.
        • Burt J.
        The role of patient experience surveys in quality assurance and improvement: a focus group study in English general practice.
        Health Expect. 2015; 18: 1982-1994
        • Klose K.
        • Kreimeier S.
        • Tangermann U.
        • Aumann I.
        • Damm K.
        • RHO Group
        Group on behalf of the RHO. Patient- and person-reports on healthcare: preferences, outcomes, experiences, and satisfaction – an essay.
        Health Econ Rev. 2016; 6: 18
        • McManus M.L.
        • Long M.C.
        • Cooper A.
        • et al.
        Variability in surgical caseload and access to intensive care services.
        Anesthesiology. 2003; 98: 1491-1496
        • Suriadi S.
        • Manns R.S.
        • Wynn M.T.
        • Partington A.
        • Karnon J.
        Measuring patient flow variations: a cross-organisational process mining approach.
        in: Ouyang C. Jung J.Y. Asia Pacific Business Process Management. Springer International Publishing, Cham, Switzerland2014: 43-58
        • Caron F.
        • Vanthienen J.
        • Vanhaecht K.
        • Limbergen E. Van
        • De Weerdt J.
        • Baesens B.
        Monitoring care processes in the gynecologic oncology department.
        Comput Biol Med. 2014; 44: 88-96
      2. Kaymak U, Mans R, Steeg TJH, Dierks M. On process mining in health care. In: Proceedings of the 2012 IEEE International Conference on Systems, Man, and Cybernetics; October 14-17, 2012; Seoul, Korea:1859-1864.

        • Mans R.
        • Schonenberg H.
        • Leonardi G.
        • et al.
        Process mining techniques: an application to stroke care.
        Stud Health Technol Inform. 2008; 136: 573-578
        • Mans R.S.
        • Schonenberg M.H.
        • Song M.
        • van der Aalst W.M.P.
        • Bakker P.J.
        Application of process mining in healthcare – a case study in a Dutch hospital.
        in: Biomedical Engineering Systems and Technologies. Springer, Berlin, Germany2009: 425-438
        • Aspland E.
        • Gartner D.
        • Harper P.
        Clinical pathway modelling: a literature review.
        Health Syst. 2021; 10: 1-23
      3. Mans R, Reijers H, van Genuchten M, Wismeijer D. Mining processes in dentistry. In: Proceedings of the 2nd ACM SIGHIT Symposium on International Health Informatics — IHI ’12; January 2012; New York, NY:379-388.

        • Caron F.
        • Vanthienen J.
        • Vanhaecht K.
        • Van Limbergen E.
        • Deweerdt J.
        • Baesens B.
        A process mining-based investigation of adverse events in care processes.
        Health Inf Manag J. 2014; 43: 16-25
        • Theis J.
        • Galanter W.
        • Boyd A.
        • Darabi H.
        Improving the in-hospital mortality prediction of diabetes ICU patients using a process mining/deep learning architecture.
        IEEE J Biomed Health Inform. 2021; 26: 388-399
        • Placidi L.
        • Boldrini L.
        • Lenkowicz J.
        • et al.
        Process mining to optimize palliative patient flow in a high-volume radiotherapy department.
        Tech Innov Patient Support Radiat Oncol. 2021; 17: 32-39
        • Arias M.
        • Rojas E.
        • Aguirre S.
        • et al.
        Mapping the Patient’s journey in healthcare through process mining.
        Int J Environ Res Public Heal. 2020; 17: 6586
        • van der Aalst W.M.P.
        • Weijters A.J.M.M.
        Process mining: a research agenda.
        Comput Ind. 2004; 53: 231-244
        • Dallagassa M.R.
        • dos Santos Garcia C.
        • Scalabrin E.E.
        • Ioshii S.O.
        • Carvalho D.R.
        Opportunities and challenges for applying process mining in healthcare: a systematic mapping study.
        J Ambient Intell Humaniz Comput. 2022; 13: 165-182
        • Erdogan T.G.
        • Tarhan A.
        Systematic mapping of process mining studies in healthcare.
        IEEE Access. 2018; 6: 24543-24567
        • Rismanchian F.
        • Lee Y.H.
        Process mining–based method of designing and optimizing the layouts of emergency departments in hospitals.
        HERD. 2017; 10: 105-120
        • Halawa F.
        • Chalil Madathil S.
        • Khasawneh M.T.
        Integrated framework of process mining and simulation–optimization for pod structured clinical layout design.
        Expert Syst Appl. 2021; 185: 115696
        • Zhou Z.
        • Wang Y.
        • Li L.
        Process mining based modeling and analysis of workflows in clinical care - a case study in a Chicago outpatient clinic.
        in: Proceedings of the 11th IEEE International Conference on Networking. Sensing and Control, Miami, FL2014: 590-595
        • Gartner D.
        • Arnolds I.V.
        • Nickel S.
        Improving hospital-wide patient scheduling decisions by clinical pathway mining.
        Stud Health Technol Inform. 2015; 216: 1066
        • Wang Q.
        Modeling and analysis of high risk patient queues.
        Eur J Oper Res. 2004; 155: 502-515
        • Cochran J.K.
        • Roche K.
        A queuing-based decision support methodology to estimate hospital inpatient bed demand.
        J Oper Res Soc. 2008; 59: 1471-1482
        • de Bruin A.M.
        • van Rossum A.C.
        • Visser M.C.
        • Koole G.M.
        Modeling the emergency cardiac in-patient flow: an application of queuing theory.
        Health Care Manag Sci. 2007; 10: 125-137
        • Cochran J.K.
        • Bharti A.
        Stochastic bed balancing of an obstetrics hospital.
        Health Care Manag Sci. 2006; 9: 31-45
        • Bretthauer K.M.
        • Heese H.S.
        • Pun H.
        • Coe E.
        Blocking in healthcare operations: a new heuristic and an application.
        Prod Oper Manag. 2011; 20: 375-391
        • Jiang L.
        • Giachetti R.E.
        A queueing network model to analyze the impact of parallelization of care on patient cycle time.
        Health Care Manag Sci. 2008; 11: 248-261
        • Creemers S.
        • Lambrecht M.
        An advanced queueing model to analyze appointment-driven service systems.
        Comput Oper Res. 2009; 36: 2773-2785
        • Mayhew L.
        • Smith D.
        Using queuing theory to analyse the Government’s 4-h completion time target in Accident and Emergency departments.
        Health Care Manag Sci. 2008; 11: 11-21
        • Song H.
        • Tucker A.L.
        • Murrell K.L.
        The impact of pooling on throughput time in discretionary work settings: an empirical investigation of emergency department length of stay. Harvard Business School Working Papers.
        • Rismanchian F.
        • Lee Y.H.
        Moment-based approximations for first- and second-order transient performance measures of an unreliable workstation.
        Oper Res. 2016; 18: 75-95
        • Gul M.
        • Guneri A.F.
        A comprehensive review of emergency department simulation applications for normal and disaster conditions.
        Comput Ind Eng. 2015; 83: 327-344
        • Mielczarek B.
        • Uziałko-Mydlikowska J.
        Application of computer simulation modeling in the health care sector: a survey.
        Simul. 2012; 88: 197-216
        • Gunal M.M.
        • Pidd M.
        Understanding accident and emergency department performance using simulation.
        Proc 2006 Winter Simul Conf. 2006; : 446-452
        • Marshall A.
        • Vasilakis C.
        • El-Darzi E.
        Length of stay-based patient flow models: recent developments and future directions.
        Health Care Manag Sci. 2005; 8: 213-220
        • Gallivan S.
        • Utley M.
        • Treasure T.
        • Valencia O.
        Booked inpatient admissions and hospital capacity: mathematical modelling study.
        BMJ. 2002; 324: 280-282
        • van Dongen B.F.
        • de Medeiros A.K.A.
        • Verbeek H.M.W.
        • Weijters A.J.M.M.
        • van der Aalst W.M.P.
        The ProM framework: a new era in process mining tool support.
        Lect Notes Comput Sci. 2005; 3536: 444-454
        • van der Aalst W.M.P.
        Process Mining: Discovery, Conformance and Enhancement of Business Processes.
        Springer-Verlag, Berlin, Germany2011
        • Pritsker A.A.B.
        • Happ W.W.
        GERT: graphical evaluation and review techniques, Part I (Fundamentals).
        J Ind Eng. 1966; 17: 267-274
        • Zimmermann J.
        Time complexity of single- and identical parallel-machine scheduling with GERT network precedence constraints.
        Math Methods Oper Res. 1999; 49: 221-238
        • Nelson R.G.
        • Azaron A.
        • Aref S.
        The use of a GERT based method to model concurrent product development processes.
        Eur J Oper Res. 2016; 250: 566-578
        • Wang C.N.
        • Yang G.K.
        • Hung K.C.
        • Chang K.H.
        • Chu P.
        Evaluating the manufacturing capability of a lithographic area by using a novel vague GERT.
        Expert Syst Appl. 2011; 38: 923-932
        • Agarwal M.
        • Sen K.
        • Mohan P.
        GERT analysis of m-consecutive-k-out-of-n systems.
        IEEE Trans Reliab. 2007; 56: 26-34
        • Li C.
        • Tang Y.
        • Li C.
        • Li L.
        A modeling approach to analyze variability of remanufacturing process routing.
        IEEE Trans Autom Sci Eng. 2013; 10: 86-98