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Enabling better management of patients: discrete event simulation combined with the STAR approach

  • Published:
Journal of the Operational Research Society

Abstract

Squeezed budgets and funding cuts are expected to become a feature of the healthcare landscape in the future, forcing decision makers such as service managers, clinicians and commissioners to find effective ways of allocating scarce resources. This paper discusses the development of a decision support toolkit (DST) that facilitates the improvement of services by identifying cost savings and efficiencies within the pathway of care. With the help of National Health Service and commercial experts, we developed a discrete event simulation model for deep vein thrombosis (DVT) patients and adapted the socio-technical allocation of resources (STAR) approach to answer crucial questions like what sort of interventions should we spend our money on? Where will we get the most value for our investment? How will we explain the choices we have made? The DST enables users to model their own services by working with the DST interface allowing users to specify local DVT services. They can input local estimates, or data of service demands and capacities, thus creating a baseline discrete event simulation model. The user can then compare the baseline with potential changes in the patient pathway in the safety of a virtual environment. By making such changes key decision makers can easily understand the impact on activity, cost, staffing levels, skill-mix, utilisation of resources and, more importantly, it allows them to find the interventions that have the highest benefit to patients and provide best value for money.

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References

  • Airoldi M, Morton A, Smith AEJ and Bevan G (2014). STAR—people powered prioritization: A 21st-century solution to allocation headaches. Medical Decision Making 34(8):965–975.

    Article  Google Scholar 

  • Bauersachs R, Berkowitz SD, Brenner B, Buller HR, Decousus H and Gallus AS (2010). Oral rivaroxaban for symptomatic venous thromboembolism. The New England Journal of Medicine 363(26):2499–2510.

    Article  Google Scholar 

  • Brailsford SC, Lattimer VA, Turnaras P and Turnbull JC (2004). Emergency and on demand health care: Modelling a large complex system. Journal of the Operational Research Society 55(1):34–42.

    Article  Google Scholar 

  • Brennan A, Chick SE and Davies R (2006). A taxonomy of model structures for economic evaluation of health technologies. Health Economics 15(12):1295–1310.

    Article  Google Scholar 

  • Codrington-Virtue A, Whittlestone P, Kelly J and Chaussalet TJ (2005). An interactive framework for developing simulation models of hospital accident and emergency services. In: Medical and care compunetics 2. Studies in health technology and informatics (114). IOS Press: Oxford, UK, pp. 277–283. ISBN 1586035207.

  • Comas M, Arrospide rigaA, Mar J, Sala M, Vilaprinyo´ E, et al. (2014). Budget impact analysis of switching to digital mammography in a population-based breast cancer screening program: A discrete event simulation model. PLoS One 9(5):e97459. doi:10.1371/journal.pone.0097459.

    Article  Google Scholar 

  • Demir E, Vasilakis C, Lebcir M and Southern D (2015). A simulation-based decision support tool for informing the management of patients with Parkinson’s disease. International Journal of Production Research 53(24):7238–7251.

    Article  Google Scholar 

  • Getsios D, Marton JP, Revankar N, Ward AJ, Wilke RJ, Rublee D, Ishak KJ and Xenakis JG (2013). Smoking cessation treatment and outcomes patterns simulation: A new framework for evaluating the potential health and economic impact of smoking cessation interventions. PharmacoEconomics 31(9):767–780.

    Article  Google Scholar 

  • Goodacre S, Sampson F, Stevenson M, Wailoo A, Sutton A, Thomas S, et al. (2006). Measurement of the clinical and cost-effectiveness of non-invasive diagnostic testing strategies for deep vein thrombosis. Health Technology Assessment 10(15):1–168.

    Article  Google Scholar 

  • Gunal M and Pidd M (2010). Discrete event simulation for performance modelling in health care: A review of the literature. Journal of Simulation 4(1):42–51.

    Article  Google Scholar 

  • Higashi H and Barendregt JJ (2011). Cost-effectiveness of total hip and knee replacements for the Australian population with osteoarthritis: Discrete-event simulation model. PLoS One 6(9):e25403. doi:10.1371/journal.pone.0025403.

    Article  Google Scholar 

  • Igarashi A, Goto R, Suwa K, Yoshikawa R, Ward AJ and Moller J (2016). Cost-effectiveness analysis of smoking cessation interventions in Japan using a discrete-event simulation. Applied Health Economics and Health Policy 14(1):77–87.

    Article  Google Scholar 

  • Katsaliaki K and Mustafee N (2011). Application of simulation within the healthcare context. Journal of the Operational Research Society 62(8):1431–1451.

    Article  Google Scholar 

  • Keeling D, Baglin T, Tait C, Watson H, Perry D, Baglin C, et al. (2011). Guidelines on oral anticoagulation with warfarin - fourth edition. British Journal of Haematology 154(3):311–324.

    Article  Google Scholar 

  • Macal CM and North MJ (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation 4(3):151–162.

    Article  Google Scholar 

  • Martinez C, Rietbrock S, Bamber L and Cohen AT (2011). Incidence of venous thromboembolism (VTE) in the general population—VTE Epidemiology Group study. In: XXIII Conference of The International Society on Thrombosis and Haematosis (ISTH).

  • Mayorga ME, Reifsnider OS, Wheeler SB and Kohler RE (2014). A discrete event simulation model to estimate population level health and economic impacts of smoking cessation interventions. In: Tolk A, Diallo SY, Ryzhov IO, Yilmaz L, Buckley S, and Miller JA (eds). Proceedings of the 2014 Winter Simulation Conference, pp. 1257–1268.

  • MIMS. (n.d.). Prescription drug database. Retrieved March 1, 2014, from http://www.mims.co.uk/.

  • NICE. (2014a). Deep vein thrombosis: Introduction. Retrieved from http://www.nhs.uk/conditions/Deep-vein-thrombosis/Pages/Introduction.aspx.

  • NICE (2014b). Cost-effectiveness modelling using patient-level simulation. Retrieved 29 January 2016 from http://www.nicedsu.org.uk/Patientlevel-simulation-TSD(2892880).htm.

  • Noble SI., Shelley MD, Coles B, Williams SM, Wilcock A and Johnson MJ (2008). Management of venous thromboembolism in patients with advanced cancer: A systematic review and meta-analysis. The Lancet Oncology 9(6):577–584.

    Article  Google Scholar 

  • ONS (2013). Office of National Statistics (ONS) mid-2013 population estimates: Pivot table analysis tool for the United Kingdom. Last accessed 29 July 2014.

  • Payment by Result (2013). Payment by Results in the NHS: tariff for 2012 to 2013. Retrieved June 23, 2013, from https://www.gov.uk/government/publications/confirmation-of-payment-by-results-pbr-arrangements-for-2012-13.

  • Payment by Result (2014). Payment by results in the NHS: Tariff for 2013 to 2014. https://www.gov.uk/government/publications/payment-by-results-pbr-operational-guidance-and-tariffs. Last accessed 06 September 2016.

  • Pilgrim H and Chilcott J (2008). Assessment of a 7-day turnaround for the reporting of cervical smear results using discrete event simulation. Journal of the Operational Research Society 59(7):902–910.

    Article  Google Scholar 

  • Pilgrim H, Tappenden P, Chilcott J, Bending M, Trueman P, Shorthouse A and Tappenden J (2009). The costs and benefits of bowel cancer service developments using discrete event simulation. Journal of the Operational Research Society 60(10):1305–1314.

    Article  Google Scholar 

  • PSSRU. (2013). Unit Costs of Health and Social Care. Retrieved June 1, 2013, from http://www.pssru.ac.uk/project-pages/unit-costs/2013/#sections.

  • Rohleder TR, Lewkonia P, Bischak DP, Duffy P and Hendijani R (2011). Using simulation modeling to improve patient flow at an outpatient orthopaedic clinic. Health Care Management Science 14(2):135–145.

    Article  Google Scholar 

  • Rose P, James R., Chapman O, and Marshall S (2011). A real world evaluation to describe the characteristics, outcomes and resource use associated with patients being managed by a secondary care based anticoagulation service. Value in Health 14(7):A387–A388.

    Article  Google Scholar 

  • Scottish Intercollegiate Guidelines Network. (2010). Prevention and management of venous thromboembolism. Retrieved March 1, 2014, from http://www.sign.ac.uk/pdf/sign122.pdf.

  • Stahl EJ, Rattner D, Wiklund R, Lester J, Beinfeld M and Gazelle S (2004). Reorganizing the system of care surrounding laparoscopic surgery: A cost-effectiveness analysis using discrete event simulation. Medical Decision Making 24(5):461–471.

    Article  Google Scholar 

  • Verjan CR, Augusto V and Xie X (2013). Economic comparison between Hospital at Home and traditional hospitalization using a simulation-based approach. Journal of Enterprise Information Management 26(1):135–153.

    Article  Google Scholar 

  • Wells PS, Anderson DR, Rodger M, Forgie M, Kearon C, Dreyer J, et al. (2003). Evaluation of D-Dimer in the Diagnosis of Suspected Deep-Vein Thrombosis. The New England Journal of Medicine 349(13):1227–1235.

    Article  Google Scholar 

  • Winter M, Keeling D, Sharpen F, Cohen H and Vallance P (2005). Procedures for the outpatient management of patients with deep vein thrombosis. Clinical and Laboratory Haematology 27(1):61–66.

    Article  Google Scholar 

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Acknowledgments

We would like to thank the following DVT Nurses for their valuable input towards establishing the DVT pathway: Jo Eggleston, University Hospitals NHS Trust; Emma Kinnaird, Bristol Royal Infirmary; Sarah Stockhall, Royal Liverpool University Hospital; Zara Lester, Royal Eye Infirmary; Carole Oughton, Broomfield Hospital and Marilyn Rees, Lansdowne Hospital.

Author information

Authors and Affiliations

  1. University of Hertfordshire, Hertfordshire, UK

    Eren Demir

  2. Pathway Communications Ltd, Cambridge, UK

    David Southern

Authors
  1. Eren Demir
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  2. David Southern
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Corresponding author

Correspondence to Eren Demir.

Appendix

Appendix

The input parameters associated with the DVT model

 

Estimate

Distribution

References

Demand

 Please specify prevalence rate of DVT within your population

104.6 per 100,0000 population

Poisson

Martinez et al (2011)

 Please specify the proportion of patients with Cancer

12.20%

Multinomial

Martinez et al (2011)

 Please specify the % of the diagnosed population with RECURRENT DVT

19%

Multinomial

Martinez et al (2011)

 Please specify the annual increase for each year (1–5) of patient arrivals

User specified

Bernoulli

N/A

Diagnosis

 What is the percentage of DVT patients arriving from the following sources?

  Percentage arriving from the GP Direct

User specified/Expert opinion

Multinomial

N/A

  Percentage arriving from Community Care

User specified/Expert opinion

Multinomial

N/A

  Percentage arriving from outpatients

User specified/Expert opinion

Multinomial

N/A

  Percentage arriving from accident and emergency (A&E)

User specified/Expert opinion

Multinomial

N/A

 What percentage of DVT patients will…

  Have a ‘Wells Score’ of greater than 2

46%

Bernoulli

Wells et al (2003)

  Have a ‘Wells Score’ of less than or equal to 2

54%

Bernoulli

Wells et al (2003)

 What percentage of DVT patients will have a…

  Positive ‘D-Dimer’ test result

30%

Bernoulli

Wells et al (2003)

  Negative ‘D-Dimer’ test result

70%

Bernoulli

Wells et al (2003)

 What % of people will have a

  Positive first ultrasound

24%

Bernoulli

Goodacre et al (2006)

  Positive repeat ultrasound

1%

Bernoulli

Goodacre et al (2006)

 What percentage of DVT patients will have

  Unprovoked

44.80%

Multinomial

Martinez et al (2011)

  Provoked

36.20%

Multinomial

Martinez et al (2011)

  Recurrent

19.0%

Multinomial

Martinez et al (2011)

Treatment

 What percentage of patients will receive the following options?

  Standard of Care (LMWH + Warfarin)

User specified/expert opinion

Bernoulli

N/A

  New treatment

User specified/expert opinion

Bernoulli

N/A

 Please define the % of patients and length of treatment

  Recurrent

 

Bauersachs et al (2010)

  3 months

3%

Multinomial

  6 months

42%

Multinomial

  12 months

55%

Multinomial

  Provoked

 

  3 months

22%

Multinomial

  6 months

63%

Multinomial

  12 months

15%

Multinomial

  Unprovoked

 

  3 months

6%

Multinomial

  6 months

63%

Multinomial

  12 months

31%

Multinomial

 Please indicate the length of treatment for cancer patients

  6 months

100%

Fixed

Noble et al (2008)

 Please indicate the number of follow-up visits required within

Warfarin

  

  3 months of treatment

9

Poisson

Winter et al (2005), Rose et al (2011), Keeling et al (2011)

  6 months of treatment

14

Poisson

  12 months of treatment

24

Poisson

 Please define the number of days LMWH injections are required for patients on 

LMWH

  

  LMWH

8 days

Fixed

(Scottish Intercollegiate Guidelines Network, 2010)

For those in ‘New Treatment’, please specify the number of first and follow-up visits for the following treatment durations:

New treatment

  

  3 months

2

Poisson

New treatment prescribing information (PI)

  6 months

2

Poisson

  12 months

2

Poisson

 On average how much staff time does it take to conduct a FIRST visit to the DVT clinic

User specified/expert opinion

Average

N/A

 Please indicate the % of time each type of staff is responsible

  Haematologist

User specified/expert opinion

Multinomial

N/A

  Nurse

User specified/expert opinion

Multinomial

N/A

  Radiologist

User specified/expert opinion

Multinomial

Expert opinion

 On average how much staff time does it take to conduct a FOLLOW-UP visit to the DVT Clinic

User specified/expert opinion

Average

Expert opinion

 Please indicate the percentage of time each type of staff is responsible

  Haematologist

User specified/expert opinion

Multinomial

Expert opinion

  Nurse

User specified/expert opinion

Multinomial

Expert opinion

  Radiologist

User specified/expert opinion

Multinomial

Expert opinion

Costing

 Cost of FIRST visit to haematology/DVT clinic

£247

Fixed

Payment by Result (2013)

 Cost of FOLLOW-UP visit to haematology/DVT clinic

£113

Fixed

Payment by Result (2013)

 Cost of FIRST visit to community DVT clinic

User specified/expert opinion

Fixed

N/A

 Cost of FOLLOW-UP visit to community DVT clinic

User specified/expert opinion

Fixed

N/A

 New treatment price per tablet

£2.10

Fixed

(MIMS)

 LMWH (NON-CANCER) cost per day

£9.77

Fixed

(MIMS), assuming an average patient weight of 80 kg

 LMWH (CANCER) cost per day month 1

£8.47

Fixed

(MIMS) assuming an average weight of a patient 69–82 kg

 Warfarin cost per day

£0.07

Fixed

(MIMS)

Salary—hourly cost

 Haematologist

£139

Fixed

PSSRU (2013)

 Nurse

£123

Fixed

PSSRU (2013)

 Radiologist

£139

Fixed

PSSRU (2013)

Number of resources

 Haematologist

User specified/expert opinion

Fixed

N/A

 Nurse

User specified/expert opinion

Fixed

N/A

 Radiologist

User specified/expert opinion

Fixed

N/A

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Demir, E., Southern, D. Enabling better management of patients: discrete event simulation combined with the STAR approach. J Oper Res Soc 68, 577–590 (2017). https://doi.org/10.1057/s41274-016-0029-y

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