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.

Voice activated remote monitoring technology for heart failure patients: Study design, feasibility and observations from a pilot randomized control trial.

BACKGROUND: Heart failure (HF) is a serious health condition, associated with high health care costs, and poor outcomes. Patient empowerment and self-care are a key component of successful HF management. The emergence of telehealth may enable providers to remotely monitor patients' statuses, support adherence to medical guidelines, improve patient wellbeing, and promote daily awareness of overall patients' health. OBJECTIVE: To assess the feasibility of a voice activated technology for monitoring of HF patients, and its impact on HF clinical outcomes and health care utilization. METHODS: We conducted a randomized clinical trial; ambulatory HF patients were randomized to voice activated technology or standard of care (SOC) for 90 days. The system developed for this study monitored patient symptoms using a daily survey and alerted healthcare providers of pre-determined reported symptoms of worsening HF. We used summary statistics and descriptive visualizations to study the alerts generated by the technology and to healthcare utilization outcomes. RESULTS: The average age of patients was 54 years, the majority were Black and 45% were women. Almost all participants had an annual income below $50,000. Baseline characteristics were not statistically significantly different between the two arms. The technical infrastructure was successfully set up and two thirds of the invited study participants interacted with the technology. Patients reported favorable perception and high comfort level with the use of voice activated technology. The responses from the participants varied widely and higher perceived symptom burden was not associated with hospitalization on qualitative assessment of the data visualization plot. Among patients randomized to the voice activated technology arm, there was one HF emergency department (ED) visit and 2 HF hospitalizations; there were no events in the SOC arm. CONCLUSIONS: This study demonstrates the feasibility of remote symptom monitoring of HF patients using voice activated technology. The varying HF severity and the wide range of patient responses to the technology indicate that personalized technological approaches are needed to capture the full benefit of the technology. The differences in health care utilization between the two arms call for further study into the impact of remote monitoring on health care utilization and patients' wellbeing.

Voice Interface Technology Adoption by Patients With Heart Failure: Pilot Comparison Study.

BACKGROUND: Heart failure (HF) is associated with high mortality rates and high costs, and self-care is crucial in the management of the condition. Telehealth can promote patients' self-care while providing frequent feedback to their health care providers about the patient's compliance and symptoms. A number of technologies have been considered in the literature to facilitate telehealth in patients with HF. An important factor in the adoption of these technologies is their ease of use. Conversational agent technologies using a voice interface can be a good option because they use speech recognition to communicate with patients. OBJECTIVE: The aim of this paper is to study the engagement of patients with HF with voice interface technology. In particular, we investigate which patient characteristics are linked to increased technology use. METHODS: We used data from two separate HF patient groups that used different telehealth technologies over a 90-day period. Each group used a different type of voice interface; however, the scripts followed by the two technologies were identical. One technology was based on Amazon's Alexa (Alexa+), and in the other technology, patients used a tablet to interact with a visually animated and voice-enabled avatar (Avatar). Patient engagement was measured as the number of days on which the patients used the technology during the study period. We used multiple linear regression to model engagement with the technology based on patients' demographic and clinical characteristics and past technology use. RESULTS: In both populations, the patients were predominantly male and Black, had an average age of 55 years, and had HF for an average of 7 years. The only patient characteristic that was statistically different (P=.008) between the two populations was the number of medications they took to manage HF, with a mean of 8.7 (SD 4.0) for Alexa+ and 5.8 (SD 3.4) for Avatar patients. The regression model on the combined population shows that older patients used the technology more frequently (an additional 1.19 days of use for each additional year of age; P=.004). The number of medications to manage HF was negatively associated with use (-5.49; P=.005), and Black patients used the technology less frequently than other patients with similar characteristics (-15.96; P=.08). CONCLUSIONS: Older patients' higher engagement with telehealth is consistent with findings from previous studies, confirming the acceptability of technology in this subset of patients with HF. However, we also found that a higher number of HF medications, which may be correlated with a higher disease burden, is negatively associated with telehealth use. Finally, the lower engagement of Black patients highlights the need for further study to identify the reasons behind this lower engagement, including the possible role of social determinants of health, and potentially create technologies that are better tailored for this population.

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.

Sensitivity of the Medication Possession Ratio to Modelling Decisions in Large Claims Databases.

OBJECTIVES: When preparing administrative medical and pharmacy claims data for analysis, decisions about data clean up and analytical approach need to be made. However, information about the effects of various modelling decisions on adherence measures such as the medication possession ratio (MPR) is limited. We address this gap with this study. METHODS: We utilized cross-sectional administrative claims data for commercially insured members filling at least two prescriptions for drugs within five classes of hypertension medication between 2008 and 2010. We divided nine modelling decisions into three categories: data scrubbing, study design, and MPR definition/calculations. We defined the base-case settings with commonly used values, varied each modelling decision singly and in combination, and measured the effects on the MPR. RESULTS: Claims data for 358,418 individuals were available for analysis. Two modelling decisions were found to be highly influential, each yielding a difference of over 25 percentage points from the base case: the decision of whether to use interval- or prescription-based study periods, and the decision of how to handle overlapping prescription claims. The effect of other decisions was smaller, with a difference of 1-9 percentage points from the base case. CONCLUSIONS: Some of the decisions considered had a large impact on the MPR. Therefore, it is important for researchers to standardize approaches for study period length and overlapping prescription claims. We also conclude that transparent reporting of modelling decisions will facilitate the interpretation of results and comparisons across studies.

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.