from The Academic Health Economists’ Blo… at http://bit.ly/2vE6CgW on February 3, 2020 at 12:06PM
Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.
Capacity constraints and time allocation in public health clinics. Health Economics [PubMed] Published 14th January 2020
Capacity constraints are a key issue in many health care markets. Capacity constraints can be due to short-run fluctuations in available labor or long-term resource scarcity where available supply does not meet demand at prevailing prices. One key issue to understand is how providers respond when capacity constraints appear. There are a number of potential options for health care providers to address capacity constraints. The first would be to decrease the number of patients seen. A second option would be to decrease quality. A third option would be to access additional funding to increase capacity. A fourth option would be to demand workers to work longer hours at no additional pay, which would be an implicit hourly wage reduction (and may not be legal).
In an attempt to find out the answer to this question, Harris, Liu and McCarthy examined data from a clinic in Tennessee. One issue with capacity constraints is that workforce allocation may be endogenous. Harris and co-authors look at a case where two nurses were removed from the clinic on selected mornings to administer flu shots at local schools. They claim that the assignment of nurses to schools was done by the local health department and those decisions were plausibly exogenous to any clinic decisions on volume or quality of care. Further, these nurses were not replaced by staff from other clinics.
The authors use data covering 16 months (i.e. two flu seasons) of visits. They first conducted a nurse-level analysis to evaluate if nurses on flu days had lower productivity. ‘FluNurse’ and ‘FluDay’ indicator variables were used. The former was used to control for whether the nurses selected to administer the flu shots at the schools were systematically more or less productive than the other nurses; the latter was used to measure the impact of the actual day when nurses were assigned to the school. The authors also conduct a visit-level analysis to see how time spent in the clinic varies on the days when nurse capacity is reduced compared to when it is not.
The authors find that the extensive margin is most important. On days when nurses visited schools, capacity is reduced by about 17%. On these days, providers do see fewer patients overall and prioritize scheduled visits over walk-ins. On the intensive margin, providers do decrease time spent with patients (by about 7%), but this time reduction is largely achieved by reducing some of the administrative aspects of the visit (i.e. expedited check-out times). The authors conclude that “providers value spending sufficient time with patients over seeing as many patients as possible.”
It is unclear whether these results would translate to other settings/countries or cases where capacity constraints are more long-term. The study does not discuss in detail how the clinic is reimbursed. A fee-for-service reimbursement may incentivize prioritizing volume over quality/time spent whereas under capitated or salaried reimbursement they may prioritize quality. Since many of the services examined by the study were provided by nurses, and nurses are more likely to be paid a salary rather than compensated based on clinic volume, it is unclear whether these results would translate to capacity constraints involving physicians.
Outcome measures for oncology alternative payment models: practical considerations and recommendations. American Journal of Managed Care [PubMed] Published 11th December 2019
Value-based payment sounds great in theory. Step 1: measure health outcomes and cost. Step 2: risk adjust to control for variability in patient health status. Step 3: pay providers more who have better outcomes and lower cost; pay providers less who have worse outcomes and higher cost. Simple, right? The answer is ‘yes’ according to many payers. A number of alternative payment models are being adapted by payers in the U.S. In oncology, the Centers for Medicare and Medicaid Services (CMS) has implemented the Oncology Care Model (OCM) to reimburse providers based on quality and cost.
This approach, however, is only valid if payers and policymakers are able to adequately measure quality. How is quality currently captured for oncology patients? A paper by Hlávka, Lin, and Neumann provides an overview of existing quality measures. Specifically they review quality metrics from the following entities: (i) OCM, (ii) the Quality Oncology Practice Initiative by the American Society of Clinical Oncologists (ASCO), (iii) the Prospective Payment System–Exempt Cancer Hospital Quality Reporting Program by CMS, (iv) the Core Quality Measures Collaborative Core Sets by CMS and America’s Health Insurance Plans (AHIP), (v) the Oncology Medical Home program by the Community Oncology Alliance, (vi) the Osteoporosis Quality Improvement Registry by the National Osteoporosis Foundation and National Bone Health Alliance, and (vii) the Oncology Qualified Clinical Data Registry by the Oncology Nursing Society. So, how well do these entities measure health outcomes?
Well, in general, most are measuring quality of care processes rather than health outcomes. Of the 142 quality measures examined, only 28 (19.7%) were outcome measures; the rest were process measures. The outcome measures of interest fell into five categories: (1) hospital admissions or emergency department visits, (2) hospice care, (3) mortality, (4) patient reported outcomes (PROs), and (5) adverse events (AEs). The paper describes in more detail how/why these metrics are used (e.g. many hospital admissions are related to chemotherapy AEs, hospice care is underutilized).
While this paper is not a methodological advance, knowing what quality measures are available is extremely important. Further, the paper cites a number of limitations of these quality metrics. First, you need reliable data to measure these measures, even if patients move across health care systems. Additionally, administrative data (e.g. claims data) often appear with a lag. Second, risk adjustment is imperfect and applying a value-based payment may incentivize providers to select more low-risk patients and avoid the sickest cancer patients. Third, if we care about patients’ opinions—which we should!—PROs are important. Collecting PROs, however, is more expensive than using administrative data. Fourth, quality measurement in general takes time and effort. My own commentary in the Journal of Clinical Pathways argues that these limitations need to be taken seriously and these costs and accuracy issues may undermine the value of value-based payment in oncology if quality is measured poorly and is costly to collect.
Modeling the impact of patient treatment preference on health outcomes in relapsing-remitting multiple sclerosis. Journal of Medical Economics [PubMed] Published January 2020
We hear a lot about patient-centered care. Intuitively, it makes a lot of sense. Patients are the end users, the customers. So we should do our best to give them the treatments they want. At the same time, patients have imperfect information and rely on physicians to help guide their decisions. However, patients may value things that—as a society—we may not be willing to pay for. For instance, empirical research shows that patients place a high value on hospital amenities. Thus, one key question to answer is whether following patient preferences is likely to result in better health outcomes.
A paper by van Eijndhoven et al. (disclosure: I am an author on this paper) aims to answer this question for patients with relapsing-remitting multiple sclerosis. The first step of the paper is to measure patient preferences across disease modifying drugs (DMDs). This was parameterized based on a discrete choice experiment of patients with multiple sclerosis. Second, a Markov model was used to estimate the impact of each individual DMD on health outcomes (i.e., number of relapses, disability progression over time, QALYs). QALYs were estimated utilities by health states defined by the Expanded Disability Status Scale (EDSS), caregiver disutility in each state, disutility per relapse, and disutility due to adverse events from the literature. Third, the authors compared two states of the world: one based on patient-driven preferences (as estimated in Step 1) and another based on current prescribing practices in the United Kingdom.
Using this approach, patient-centered prescribing practices lead to 6.8% fewer relapses and a 4.6% increase in QALYs. Applied to the UK population level, this would result in almost 37,000 avoided relapses and over 44,000 discounted QALYs over a 50 year period. Additionally, disease progression was slowed. For the typical patients, EDSS was -0.16 less each year under the patient-centered prescribing compared to the current market shares.
There are a few reasons for this finding. First, under current treatment patterns, many patients are not treated. About 21% of patients with RRMS in the UK do not receive DMDs. Access to neurologist care is often difficult. Second, patients do have a strong preference for more effective, newer treatments. Previous research indicates that prescribers in England generally viewed NICE guidelines as mandatory criteria they were obligated to follow, whereas neurologists in Scotland and Wales were more varied.
This study did have a number of limitations. First, the study did not examine costs. Patients may prefer more effective treatments, but this may have cost implications for the UK health system. Second, the impact of treatments was based on clinical trial data. DMDs may be more or less effective in the real world, particularly if adherence is suboptimal. Third, once people reach high levels of disability (EDSS ≥7), the study assumed that they were treated with best supportive care. Thus, the estimates may be conservative if treatment is more aggressive. Fourth, the treatment options were based on currently approved DMDs. In the real-world, however, new treatments may become available and the actual health trajectories are likely to deviate from our model.
While this study does not answer what is the optimal treatment mix for a country, we do see evidence that patient-preferred treatments are highly related to the health benefits received. Thus—in the case of multiple sclerosis—physicians should not fear that shared decision-making with patients will result in worse outcomes. On the country, better health outcomes could be expected from shared decision-making.