|HEALTHCARE OPERATIONS WORKSHOP, JULY 15, KOÇ UNIVERSITY, CASZ48|
|9:00-9:30||Welcome + Coffee|
|9:30-10:30||Christos Vasilakis||University of Bath||The development of an automated surveillance system for surgical site infections||Ã–nder ErgÃ¶nÃ¼l, KoÃ§ University Medical School|
|11:00-12:00||Feryal Erhun||University of Cambridge||Lessons from Leading Health Systems: Bending Healthcare Costs for CABG with Improved Processes||Ã–zlem Olgu, KoÃ§ University|
|13:30-14:30||Tolga Tezcan||London Business School||Yardstick Competition for Service Systems||Emine YaylalÄ±, CDC|
|15:00-16:00||Enis KayÄ±ÅŸ||Ã–zyeÄŸin University||Prescriptive and Predictive Models for Next-Day Operating Room Scheduling||Sakine Batun, METU|
|16:00-16:30||Farewell + Coffee|
â€œThe development of an automated surveillance system for surgical site infectionsâ€
Christos Vasilakis, University of Bath
Surgical site infection (SSI), which occurs when pathogens enter the surgical incision, is a substantial cause of excess morbidity and mortality with a consequent direct impact on quality and cost of patient care. The study was motivated by the observation that, despite the criticality of SSI surveillance and reporting, the process is typically highly variable, being best characterized as complex, (overly) reliant on judgment and consequently unreliable. The research comprised two elements. First, we studied the SSI reporting process itself capturing its key technical and behavioral characteristics. Although the qualitative and quantitative data we collected originate from the national surveillance service in England, there are many similarities with the surveillance practices and systems in place in other countries, including the US. Second, our observations regarding the variability and potential unreliability of the extant system motivated the development of an analytical method for identifying infections from routinely collected data. The method was developed using a series of regression models fitted to the data. Three different performance measures were used to identify the most discriminating model regarding SSI detection. Our analytical method allows the expansion of SSI surveillance across specialties and its future integration into a software tool that would facilitate rapid reporting of recent SSI rates.
â€œLessons from Leading Health Systems: Bending Healthcare Costs for CABG with Improved Processesâ€
Feryal Erhun, University of Cambridge
BACKGROUND: Despite growing incentives to improve value, most hospitals still lack accurate information on their costs to treat medical conditions. We calculated and compared the cost of performing coronary artery bypass graft surgery (CABG) at three leading hospitals known for providing high value care.
METHOD: We used time-driven activity-based costing to compare the average direct costs of CABG at three non-academic teaching hospitals – two sites in the U.S. (site 1 and site 2) and one site in India (site 3). Site 1 performs only cardiac procedures; site 2 emphasizes lean management practices; and site 3 applies systems engineering tools. We used direct observation and staff interviews to collect average staff times as well as equipment and materials used for uncomplicated CABG surgery. We calculated the unit costs paid for each employee type and for purchased materials. We used variance analysis to identify cost differentials due to employee efficiencies, employee skill mix, and unit costs of people, excluding indirect (general and administrative) costs from the analysis. We simultaneously pursued a separately reported comparison of risk-adjusted outcomes.
RESULTS: As expected, site 3 had the lowest cost for an uncomplicated CABG; under 10% of both U.S. sites. The risk-adjusted mortality for all three hospitals (separately reported) was better than the U.S. average. After removing the impact of the lower prices paid for employees and space in site 3, its standardized cost was still significantly lower.Â Practices such as tiered care delivery, task shifting, and sweating resources help site 3 achieve such low production costs.
CONCLUSIONS and RELEVANCE: The hospital leader and clinicians at site 3 operate with a pervasive â€œmindsetâ€ to zealously seek out and remove unnecessary cost because â€œour patients must sell their homes to lie in our beds.â€ If site 3â€™s efficiencies were implemented in the U.S. hospitals, their costs could decline significantly. Site 3â€™s mindset for relentless cost reduction illustrates the opportunity in the U.S. to achieve dramatic cost improvements without sacrificing patient outcomes.
â€œYardstick Competition for Service Systemsâ€
Tolga Tezcan, London School of Business
Yardstick competition is a regulatory scheme for local monopolists (e.g., hospitals),
where the monopolistâ€™s reimbursement is linked to its performance relative to other equivalent monopolists. This regulatory scheme is known to work well in providing cost-reduction incentives and offers the theoretical underpinning behind the hospital prospective reimbursement system used throughout the developed world. This paper investigates how yardstick competition performs in service systems (e.g., hospital emergency departments), where in addition to incentivizing cost reduction, the regulatorâ€™s goal is to provide incentives to reduce customer waiting times. We show that i) the form of yardstick competition used in practice results in inefficiently long waiting times; ii) yardstick competition can be appropriately modified to achieve the dual goal of cost and waiting-time reduction, and present several extensions that help guide on how it could be used in practice.
â€œPrescriptive and Predictive Models for Next-Day Operating Room Schedulingâ€
Enis KayÄ±ÅŸ, Ã–zyeÄŸin University
Operating room (OR) is one of the most critical and expensive resources in hospitals. OR scheduling has a significant impact on many key performance criteria for healthcare delivery systems such as resource utilization, patient satisfaction, and staff overtime. However, this is a challenging problem due to many sources of uncertainties, multiple stakeholders, and complexity of operations. Data resulting from increasingly widespread deployment of electronic health record (EHR) systems are starting to provide an important foundation to improve operational efficiency of the ORs using data driven models.
First, I will present the daily scheduling problem of a single OR with uncertain surgery durations. Our aim is to find the optimum sequence and scheduled starting times of the surgeries to minimize weighted sum of expected patient waiting times and OR idle times. In addition to analytical results providing useful insights on the characteristics of the optimum solutions, we develop scheduling heuristics and compare their performance. Our heuristics provide insights for the practitioners that do not have the resources to implement more advanced models.
In the second part, I will present a predictive analytics model to improve traditional surgery duration estimates using a combination of operational, temporal, and staff related factors. Using two years of detailed operational data from an EHR system, we conclude that even though the inherent variability in surgery duration estimates is high, improving such estimates is still possible. In order to take advantage of these improved estimates, however, we need to modify existing OR scheduling models.