On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Sarah Zheng who has a PhD from Boston University. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.
Design for performance: studies on cost and quality in U.S. health care
Z. Justin Ren, Kimberley H. Geissler, Janelle Heineke, Anita Tucker
In the context of your PhD research, what does ‘design for performance’ mean?
“Design for performance” is a further step in managing healthcare from “pay for performance”, on which there has been decades of attention paid among practitioners and academics. Despite the long effort on “pay for performance”, the core challenge remains how to properly incentivize patients, clinicians and staff to align their behaviors with optimal, safe and cost-effective, patient-centric care. This dissertation suggests an important set of issues to consider around “design for performance” at the system and process levels.
At the system level, under what conditions does cost-sharing lead to lower total costs without reducing quality of care? Previous literature has studied contract theory and mechanism design in varied industry settings (Guajardo et al. 2012), yet very few are studied in the healthcare domain where insurance plans are offered to patients under different contract arrangements. It remains unclear whether certain contract design at such settings may lead to desired outcomes (e.g., low healthcare spending). At the process level, under what conditions and to what extent does excellent internal supply operations result in superior hospital performance? Industrial studies suggest that reliable, efficient internal supply chains that are integrated with production yield better financial and quality performance for manufacturing companies (Droge et al. 2004, Flynn et al. 2010). However, there is scant quantitative research on the impact of support departments in hospitals (Tucker et al. 2008, Fredendall et al. 2009). Studies are needed to understand the extent to which support departments impact patient care outcomes, such as adverse events.
How was quality captured in the data that you used in your analyses?
In Chapter 3 of my thesis, I studied the impact of internal service quality on one particular quality performance metric: adverse events. Specifically, it is a rate variable that is calculated by the sum of adverse events (i.e., patient falls with injury and pressure ulcers) on the unit that month divided by the number of patient days on the unit that month, which is then multiplied by 1,000. The hospital collects these data monthly. The adverse event data come from both patient record reviews and incident reports in the hospital’s safety reporting system, as is typical of this type of data (Lake and Cheung 2006). The error event data are audited internally as well as reported to CMS (Zheng et al. 2017).
This is a unique opportunity to study quality as most healthcare operations research has relied on publicly available, hospital-level quality data, such as patient mortality (e.g., Senot et al. 2015, KC and Terwiesch 2011)—which is a blunt measure of quality—or process of care measures (e.g., Boyer et al. 2012, Gardner et al. 2015, Senot et al. 2015), which have been criticized in the healthcare literature for their weak connection to clinical outcomes (Patterson et al. 2010).
You complemented your quantitative analysis with some qualitative interviews – was this a valuable exercise?
Yes, definitely. To understand further the role patients (and the patient-physician dyad) play in deciding the usage of imaging studies, I conducted in-depth conversations with both physicians and patients. Specifically, I interviewed three physicians (i.e., hospitalist, primary care provider) and one imaging technician with the average conversation time of 70 mins. I also interviewed six patients with the average conversation time of 20 mins.
I found that patients did play a role in deciding the usage of imaging studies in the way that high-deductible health plan (HDHP) patients are less likely to demand imaging studies than non-HDHP patients. However, as patients cannot distinguish low-value care from high-value care, HDHP patients avoid patient care in general. This is consistent with previous literature on patient cost-sharing and HDHPs where patients indiscriminately reduce medical care (Hibbard et al. 2008, Lohr et al. 1986). It further suggests that HDHP may be a blunt instrument, reducing all diagnostic imaging, rather than helping physicians and patients choose high-value imaging.
Did any of your findings about high-deductible health plans stand out as different from previous studies?
I wouldn’t say different but more like complementary. Previous studies found HDHPs have different impacts depending on the site and type of care (Haviland et al. 2015, Wharam et al. 2013, Bundorf 2012, Nair et al. 2009, Waters et al. 2011, Hibbard et al. 2008, Busch et al. 2006, Rowe et al. 2008, Parente et al. 2004). By explicitly testing associations between HDHP enrollment and diagnostic imaging, we provide a more complete picture for policymakers in making guidelines related to HDHP plans. Our results suggest that increases in HDHP enrollment may contribute to a slow in the growth of diagnostic imaging utilization. However, increased cost-sharing may not allow patients to differentiate between high-value and low-value utilization, and better patient awareness and education should be a crucial part of any reductions in diagnostic imaging utilization (Zheng et al. 2016).
‘Internal service quality’ is a term that doesn’t often appear in health economics journals – should researchers be dedicating more attention to this?
Yes. In our study we find improved internal service quality to be a particularly novel driver of reduced adverse events because it is not obvious a priori that support departments—most of which are not clinical in nature—could have a significant impact on clinical outcomes. In particular, we find that improving the overall average internal service quality received by a nursing unit by 0.1 on a 5-point scale is associated with a 38% reduction in adverse events per nursing unit, which has roughly the same benefit for reducing adverse events as increasing staffing on that unit by nearly one full-time equivalent nurse. In the hospital that we study, the average salary of a support service technician is lower than the average salary of a nurse. Thus, hospitals might be able to improve quality of care at a lower cost by increasing support staff to relieve the burden on nurses (Zheng et al. 2017). More studies are needed in this area to explore further internal service quality as a viable and cost-effective means to improve clinical performance.