An empirical comparison of Markov cohort modeling and discrete event simulation in a capacity-constrained health care setting.

OBJECTIVES: To empirically compare Markov cohort modeling (MM) and discrete event simulation (DES) with and without dynamic queuing (DQ) for cost-effectiveness (CE) analysis of a novel method of health services delivery where capacity constraints predominate. METHODS: A common data-set comparing usual orthopedic care (UC) to an orthopedic physiotherapy screening clinic and multidisciplinary treatment service (OPSC) was used to develop a MM and a DES without (DES-no-DQ) and with DQ (DES-DQ). Model results were then compared in detail. RESULTS: The MM predicted an incremental CE ratio (ICER) of $495 per additional quality-adjusted life-year (QALY) for OPSC over UC. The DES-no-DQ showed OPSC dominating UC; the DES-DQ generated an ICER of $2342 per QALY. CONCLUSIONS: The MM and DES-no-DQ ICER estimates differed due to the MM having implicit delays built into its structure as a result of having fixed cycle lengths, which are not a feature of DES. The non-DQ models assume that queues are at a steady state. Conversely, queues in the DES-DQ develop flexibly with supply and demand for resources, in this case, leading to different estimates of resource use and CE. The choice of MM or DES (with or without DQ) would not alter the reimbursement of OPSC as it was highly cost-effective compared to UC in all analyses. However, the modeling method may influence decisions where ICERs are closer to the CE acceptability threshold, or where capacity constraints and DQ are important features of the system. In these cases, DES-DQ would be the preferred modeling technique to avoid incorrect resource allocation decisions.

The Efficiency of Increasing the Capacity of Physiotherapy Screening Clinics or Traditional Medical Services to Address Unmet Demand in Orthopaedic Outpatients: A Practical Application of Discrete Event Simulation with Dynamic Queuing.

BACKGROUND: Hospital outpatient orthopaedic services traditionally rely on medical specialists to assess all new patients to determine appropriate care. This has resulted in significant delays in service provision. In response, Orthopaedic Physiotherapy Screening Clinics and Multidisciplinary Services (OPSC) have been introduced to assess and co-ordinate care for semi- and non-urgent patients. OBJECTIVES: To compare the efficiency of delivering increased semi- and non-urgent orthopaedic outpatient services through: (1) additional OPSC services; (2) additional traditional orthopaedic medical services with added surgical resources (TOMS + Surg); or (3) additional TOMS without added surgical resources (TOMS - Surg). METHODS: A cost-utility analysis using discrete event simulation (DES) with dynamic queuing (DQ) was used to predict the cost effectiveness, throughput, queuing times, and resource utilisation, associated with introducing additional OPSC or TOMS ± Surg versus usual care. RESULTS: The introduction of additional OPSC or TOMS (±surgery) would be considered cost effective in Australia. However, OPSC was the most cost-effective option. Increasing the capacity of current OPSC services is an efficient way to improve patient throughput and waiting times without exceeding current surgical resources. An OPSC capacity increase of ~100 patients per month appears cost effective (A$8546 per quality-adjusted life-year) and results in a high level of OPSC utilisation (98 %). CONCLUSION: Increasing OPSC capacity to manage semi- and non-urgent patients would be cost effective, improve throughput, and reduce waiting times without exceeding current surgical resources. Unlike Markov cohort modelling, microsimulation, or DES without DQ, employing DES-DQ in situations where capacity constraints predominate provides valuable additional information beyond cost effectiveness to guide resource allocation decisions.