Rationing scarce healthcare capacity: A study of the ventilator allocation guidelines during the COVID-19 pandemic

In the United States, even though national guidelines for allocating scarce healthcare resources are lacking, 26 states have specific ventilator allocation guidelines to be invoked in case of a shortage. While several states developed their guidelines in response to the recent COVID-19 pandemic, New York State developed these guidelines in 2015 as "pandemic influenza is a foreseeable threat, one that we cannot ignore." The primary objective of this study is to assess the existing procedures and priority rules in place for allocating/rationing scarce ventilator capacity and propose alternative (and improved) priority schemes. We first build machine learning models using inpatient records of COVID-19 patients admitted to New York-Presbyterian/Columbia University Irving Medical Center and an affiliated community health center to predict survival probabilities as well as ventilator length-of-use. Then, we use the resulting point estimators and their uncertainties as inputs for a multiclass priority queueing model with abandonments to assess three priority schemes: (i) SOFA-P (Sequential Organ Failure Assessment based prioritization), which most closely mimics the existing practice by prioritizing patients with sufficiently low SOFA scores; (ii) ISP (incremental survival probability), which assigns priority based on patient-level survival predictions; and (iii) ISP-LU (incremental survival probability per length-of-use), which takes into account survival predictions and resource use duration. Our findings highlight that our proposed priority scheme, ISP-LU, achieves a demonstrable improvement over the other two alternatives. Specifically, the expected number of survivals increases and death risk while waiting for ventilator use decreases. We also show that ISP-LU is a robust priority scheme whose implementation yields a Pareto-improvement over both SOFA-P and ISP in terms of maximizing saved lives after mechanical ventilation while limiting racial disparity in access to the priority queue.

The bird's-eye view: A data-driven approach to understanding patient journeys from claims data

OBJECTIVE

In preference-sensitive conditions such as back pain, there can be high levels of variability in the trajectory of patient care. We sought to develop a methodology that extracts a realistic and comprehensive understanding of the patient journey using medical and pharmaceutical insurance claims data.

MATERIALS AND METHODS

We processed a sample of 10 000 patient episodes (comprised of 113 215 back pain-related claims) into strings of characters, where each letter corresponds to a distinct encounter with the healthcare system. We customized the Levenshtein edit distance algorithm to evaluate the level of similarity between each pair of episodes based on both their content (types of events) and ordering (sequence of events). We then used clustering to extract the main variations of the patient journey.

RESULTS

The algorithm resulted in 12 comprehensive and clinically distinct patterns (clusters) of patient journeys that represent the main ways patients are diagnosed and treated for back pain. We further characterized demographic and utilization metrics for each cluster and observed clear differentiation between the clusters in terms of both clinical content and patient characteristics.

DISCUSSION

Despite being a complex and often noisy data source, administrative claims provide a unique longitudinal overview of patient care across multiple service providers and locations. This methodology leverages claims to capture a data-driven understanding of how patients traverse the healthcare system.

CONCLUSIONS

When tailored to various conditions and patient settings, this methodology can provide accurate overviews of patient journeys and facilitate a shift toward high-quality practice patterns.

The Value of Shorter Initial Opioid Prescriptions: A Simulation Evaluation

BACKGROUND

During the period from 1999 to 2016, more than 350,000 Americans died from overdoses related to the use of prescription opioids. To the extent that supply is directly related to overprescribing, policy interventions aimed at changing prescriber behavior, such as the recent Centers for Disease Control and Prevention guideline, are clearly warranted. Although these could plausibly reduce the prevalence of opioid overuse and dependency, little is known about their economic and health-related impacts.

OBJECTIVE

The aim of this study was to quantify the efficacy of a policy intervention aimed at reducing the length of initial opioid prescriptions.

STUDY DESIGN AND METHODS

A Markov decision process model was fitted on a retrospective cohort of 827,265 patients, and patient cost and health trajectories were simulated over a 24-month period. The model's parameters were based on patients who received short (≤ 3 days) or long (> 7 days) initial opioid prescriptions, matched using propensity score methods.

STUDY POPULATION

All active-duty US Army soldiers from 2011 to 2014; the data contained detailed medical and administrative information on over 11 million soldier-months corresponding to 827,265 individual soldiers.

MAIN OUTCOME MEASURE

Overall costs of a policy change, quality-adjusted life-years (QALYs) gained, and $/QALY gained.

RESULTS

Over a 2-year horizon, a reassignment of 10,000 patients to short initial duration would generate a cost saving in the vicinity of $3.1 million (excluding program costs), and would also lead to an estimated 4451 additional opioid-free months, i.e. months without any opioid prescriptions.

CONCLUSION

The analysis found that efforts to change prescriber behavior can be cost effective, and further studies into the implementation of such policies are warranted.

Stated Pain Levels, Opioid Prescription Volume, and Chronic Opioid Use Among United States Army Soldiers

INTRODUCTION

The use of opioids has increased drastically over the past few years and decades. As a result, concerns have mounted over serious outcomes associated with chronic opioid use (COU), including dependency and death. A greater understanding of the factors that are associated with COU will be critical if prescribers are to navigate potentially competing objectives to provide compassionate care, while reducing the overall opioid use problem. In this study, we study pain levels and opioid prescription volumes and their effects on the risk of COU.This study leveraged passive data sources that support automated decision support systems (DSSs) currently employed in a large military population. The models presented compute monthly, person-specific, adjusted probability of subsequent COT and could potentially provide critical decision support for clinicians engaged in pain management.

MATERIALS AND METHODS

The study population included all outpatient presentations at military medical facilities worldwide among active duty United States Army soldiers during July 2011 to September 2014 (17,664,006 encounters; population N = 552,193). We conducted a retrospective cohort study of this population and employed longitudinal data and a discrete time multivariable logistic regression model to compute COT probability scores. The contribution of pain scores and opioid prescription quantities to the probability of COT represented analytic foci.

RESULTS

There were 13,891 subjects (2.5%) who experienced incident COT during the observed time period. Statistically significant interactions between pain scores and prescription quantity were present, in addition to effects of multiple other control variables. Counts of monthly opioid prescriptions and maximum stated pain scores per month were each positively associated with COT. A wide range in individual COT risk scores was evident. The effect of prescription volume on the COT risk was larger than the effect of the pain score, and the combined effect of larger pain scores and increased prescription quantity was moderated by the interaction term.

CONCLUSIONS

The results verified that passive data on the US Army can support a robust COT risk computation in this population. The individual, adjusted risk level requires statistical analyses to be fully understood. Because the same data sources drive current military DSSs, this work provides the potential basis for new, evidence-based decision support resources for military clinicians. The strong, independent impact of increasing opioid prescription counts on the COT risk reinforces the importance of exploring alternatives to opioids in pain management planning. It suggests that changing provider behavior through enhanced decision support could help reduce COT rates.

On a Firm’s Optimal Response to Pressure for Gender Pay Equity

We present a theory of how a rational, profit-maximizing firm would respond to pressure for gender pay equity by strategically distributing raises to reduce the pay gap between its female and male employees at minimum cost. Using formal analysis and pay data from a real employer, we show that (1) employees in low-paying jobs and whose pay-related observables are similar to those of men at the firm are most likely to get raises; (2) counterintuitively, some men may get raises, and giving raises to certain women would increase the pay gap; and (3) a firm can reduce the gender pay gap as measured by a much larger percentage than the overall increase in pay to women at the firm. We also identify the conditions under which a firm could “explain away” a gender pay gap using other pay-related observables, such as job category, as well as the conditions under which this strategy would backfire. Our paper helps explain some empirical puzzles, such as the tendency for some men to get raises after gender equity pay reviews, and yields a rich set of implications for empirical research and practice.

The online appendix is available at https://doi.org/10.1287/orsc.2018.1248 .

Understanding Adherence and Prescription Patterns Using Large-Scale Claims Data

BACKGROUND

Advanced computing capabilities and novel visual analytics tools now allow us to move beyond the traditional cross-sectional summaries to analyze longitudinal prescription patterns and the impact of study design decisions. For example, design decisions regarding gaps and overlaps in prescription fill data are necessary for measuring adherence using prescription claims data. However, little is known regarding the impact of these decisions on measures of medication possession (e.g., medication possession ratio). The goal of the study was to demonstrate the use of visualization tools for pattern discovery, hypothesis generation, and study design.

METHOD

We utilized EventFlow, a novel discrete event sequence visualization software, to investigate patterns of prescription fills, including gaps and overlaps, utilizing large-scale healthcare claims data. The study analyzes data of individuals who had at least two prescriptions for one of five hypertension medication classes: ACE inhibitors, angiotensin II receptor blockers, beta blockers, calcium channel blockers, and diuretics. We focused on those members initiating therapy with diuretics (19.2%) who may have concurrently or subsequently take drugs in other classes as well. We identified longitudinal patterns in prescription fills for antihypertensive medications, investigated the implications of decisions regarding gap length and overlaps, and examined the impact on the average cost and adherence of the initial treatment episode.

RESULTS

A total of 790,609 individuals are included in the study sample, 19.2% (N = 151,566) of whom started on diuretics first during the study period. The average age was 52.4 years and 53.1% of the population was female. When the allowable gap was zero, 34% of the population had continuous coverage and the average length of continuous coverage was 2 months. In contrast, when the allowable gap was 30 days, 69% of the population showed a single continuous prescription period with an average length of 5 months. The average prescription cost of the period of continuous coverage ranged from US$3.44 (when the maximum gap was 0 day) to US$9.08 (when the maximum gap was 30 days). Results were less impactful when considering overlaps.

CONCLUSIONS

This proof-of-concept study illustrates the use of visual analytics tools in characterizing longitudinal medication possession. We find that prescription patterns and associated prescription costs are more influenced by allowable gap lengths than by definitions and treatment of overlap. Research using medication gaps and overlaps to define medication possession in prescription claims data should pay particular attention to the definition and use of gap lengths.