Mobile health clinic model in the COVID-19 pandemic: lessons learned and opportunities for policy changes and innovation.

BACKGROUND: Mobile Clinics represent an untapped resource for our healthcare system. The COVID-19 pandemic has exacerbated its limitations. Mobile health clinic programs in the US already play important, albeit under-appreciated roles in the healthcare system. They provide access to healthcare especially for displaced or isolated individuals; they offer versatility in the setting of a damaged or inadequate healthcare infrastructure; and, as a longstanding community-based service delivery model, they fill gaps in the healthcare safety-net, reaching social-economically underserved populations in both urban and rural areas. Despite an increasing body of evidence of the unique value of this highly adaptable model of care, mobile clinics are not widely supported. This has resulted in a missed opportunity to deploy mobile clinics during national emergencies such as the COVID-19 pandemic, as well as using these already existing, and trusted programs to overcome barriers to access that are experienced by under-resourced communities. MAIN TEXT: In March, the Mobile Healthcare Association and Mobile Health Map, a program of Harvard Medical School's Family Van, hosted a webinar of over 300 mobile health providers, sharing their experiences, challenges and best practices of responding to COVID 19. They demonstrated the untapped potential of this sector of the healthcare system in responding to healthcare crises. A Call to Action: The flexibility and adaptability of mobile clinics make them ideal partners in responding to pandemics, such as COVID-19. In this commentary we propose three approaches to support further expansion and integration of mobile health clinics into the healthcare system: First, demonstrate the economic contribution of mobile clinics to the healthcare system. Second, expand the number of mobile clinic programs and integrate them into the healthcare infrastructure and emergency preparedness. Third, expand their use of technology to facilitate this integration. CONCLUSIONS: Understanding the economic and social impact that mobile clinics are having in our communities should provide the evidence to justify policies that will enable expansion and optimal integration of mobile clinics into our healthcare delivery system, and help us address current and future health crises.

Applying Machine Learning Algorithms to Segment High-Cost Patient Populations.

BACKGROUND: Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients. OBJECTIVE: To evaluate the performance of three different machine learning algorithms for identifying subgroups of high-cost patients. DESIGN: We applied three different clustering algorithms-connectivity-based clustering using agglomerative hierarchical clustering, centroid-based clustering with the k-medoids algorithm, and density-based clustering with the OPTICS algorithm-to a clinical and administrative dataset. We then examined the extent to which each algorithm identified subgroups of patients that were (1) clinically distinct and (2) associated with meaningful differences in relevant utilization metrics. PARTICIPANTS: Patients enrolled in a national Medicare Advantage plan, categorized in the top decile of spending (n = 6154). MAIN MEASURES: Post hoc discriminative models comparing the importance of variables for distinguishing observations in one cluster from the rest. Variance in utilization and spending measures. KEY RESULTS: Connectivity-based, centroid-based, and density-based clustering identified eight, five, and ten subgroups of high-cost patients, respectively. Post hoc discriminative models indicated that density-based clustering subgroups were the most clinically distinct. The variance of utilization and spending measures was the greatest among the subgroups identified through density-based clustering. CONCLUSIONS: Machine learning algorithms can be used to segment a high-cost patient population into subgroups of patients that are clinically distinct and associated with meaningful differences in utilization and spending measures. For these purposes, density-based clustering with the OPTICS algorithm outperformed connectivity-based and centroid-based clustering algorithms.

Subgroups of High-Cost Medicare Advantage Patients: an Observational Study.

BACKGROUND: There is a growing focus on improving the quality and value of health care delivery for high-cost patients. Compared to fee-for-service Medicare, less is known about the clinical composition of high-cost Medicare Advantage populations. OBJECTIVE: To describe a high-cost Medicare Advantage population and identify clinically and operationally significant subgroups of patients. DESIGN: We used a density-based clustering algorithm to group high-cost patients (top 10% of spending) according to 161 distinct demographic, clinical, and claims-based variables. We then examined rates of utilization, spending, and mortality among subgroups. PARTICIPANTS: Sixty-one thousand five hundred forty-six Medicare Advantage beneficiaries. MAIN MEASURES: Spending, utilization, and mortality. KEY RESULTS: High-cost patients (n = 6154) accounted for 55% of total spending. High-cost patients were more likely to be younger, male, and have higher rates of comorbid illnesses. We identified ten subgroups of high-cost patients: acute exacerbations of chronic disease (mixed); end-stage renal disease (ESRD); recurrent gastrointestinal bleed (GIB); orthopedic trauma (trauma); vascular disease (vascular); surgical infections and other complications (complications); cirrhosis with hepatitis C (liver); ESRD with increased medical and behavioral comorbidity (ESRD+); cancer with high-cost imaging and radiation therapy (oncology); and neurologic disorders (neurologic). The average number of inpatient days ranged from 3.25 (oncology) to 26.09 (trauma). Preventable spending (as a percentage of total spending) ranged from 0.8% (oncology) to 9.5% (complications) and the percentage of spending attributable to prescription medications ranged from 7.9% (trauma and oncology) to 77.0% (liver). The percentage of patients who were persistently high-cost ranged from 11.8% (trauma) to 100.0% (ESRD+). One-year mortality ranged from 0.0% (liver) to 25.8% (ESRD+). CONCLUSIONS: We identified clinically distinct subgroups of patients within a heterogeneous high-cost Medicare Advantage population using cluster analysis. These subgroups, defined by condition-specific profiles and illness trajectories, had markedly different patterns of utilization, spending, and mortality, holding important implications for clinical strategy.

Implementation of a Health Plan Program for Switching From Analogue to Human Insulin and Glycemic Control Among Medicare Beneficiaries With Type 2 Diabetes.

Importance: Prices for newer analogue insulin products have increased. Lower-cost human insulin may be effective for many patients with type 2 diabetes. Objective: To evaluate the association between implementation of a health plan-based intervention of switching patients from analogue to human insulin and glycemic control. Design, Setting, and Participants: A retrospective cohort study using population-level interrupted times series analysis of members participating in a Medicare Advantage and prescription drug plan operating in 4 US states. Participants were prescribed insulin between January 1, 2014, and December 31, 2016 (median follow-up, 729 days). The intervention began in February 2015 and was expanded to the entire health plan system by June 2015. Exposures: Implementation of a health plan program to switch patients from analogue to human insulin. Main Outcomes and Measures: The primary outcome was the change in mean hemoglobin A1c (HbA1c) levels estimated over three 12-month periods: preintervention (baseline) in 2014, intervention in 2015, and postintervention in 2016. Secondary outcomes included rates of serious hypoglycemia or hyperglycemia using ICD-9-CM and ICD-10-CM diagnostic codes. Results: Over 3 years, 14 635 members (mean [SD] age: 72.5 [9.8] years; 51% women; 93% with type 2 diabetes) filled 221 866 insulin prescriptions. The mean HbA1c was 8.46% (95% CI, 8.40%-8.52%) at baseline and decreased at a rate of -0.02% (95% CI, -0.03% to -0.01%; P <.001) per month before the intervention. There was an association between the start of the intervention and an overall HbA1c level increase of 0.14% (95% CI, 0.05%-0.23%; P = .003) and slope change of 0.02% (95% CI, 0.01%-0.03%; P < .001). After the completion of the intervention, there were no significant differences in changes in the level (0.08% [95% CI, -0.01% to 0.17%]) or slope (<0.001% [95% CI, -0.008% to 0.010%]) of mean HbA1c compared with the intervention period (P = .09 and P = 0.81, respectively). For serious hypoglycemic events, there was no significant association between the start of the intervention and a level (2.66/1000 person-years [95% CI, -3.82 to 9.13]; P = .41) or slope change (-0.66/1000 person-years [95% CI, -1.59 to 0.27]; P = .16). The level (1.64/1000 person-years [95% CI, -4.83 to 8.11]; P = .61) and slope (-0.23/1000 person-years [95% CI, -1.17 to 0.70]; P = .61) changes in the postintervention period were not significantly different compared with the intervention period. The baseline rate of serious hyperglycemia was 22.33 per 1000 person-years (95% CI, 12.70-31.97). For the rate of serious hyperglycemic events, there was no significant association between the start of the intervention and a level (4.23/1000 person-years [95% CI, -8.62 to 17.08]; P = .51) or slope (-0.51/1000 person-years [95% CI, -2.37 to 1.34]; P = .58) change. Conclusions and Relevance: Among Medicare beneficiaries with type 2 diabetes, implementation of a health plan program that involved switching patients from analogue to human insulin was associated with a small increase in population-level HbA1c.

Characterizing Sleep Issues Using Twitter.

BACKGROUND: Sleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon. OBJECTIVE: Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues. METHODS: Twitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, "can't sleep", Ambien, and others. Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues. RESULTS: User data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter (P=.04), had fewer friends (P<.001), and fewer followers (P<.001) compared to others, after adjusting for the length of time each user's account has been active. Sleep group users were more active during typical sleeping hours than others, which may suggest they were having difficulty sleeping. Sleep group users also had significantly lower sentiment in their tweets (P<.001), indicating a possible relationship between sleep and pyschosocial issues. CONCLUSIONS: We have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered.

Secondary uses of electronic health record data: benefits and barriers.

In the primary use of health data, patient health information in electronic health records (EHRs) directly informs each individual's care. In secondary use, patient data would be aggregated to improve health care delivery, yet several technological and policy barriers may slow implementation-but may be amenable to intervention.

Health Costs And Financing: Challenges And Strategies For A New Administration.

It is likely that 2021 will be a dynamic year for US health care policy. There is pressing need and opportunity for health reform that helps achieve better access, affordability, and equity. In this commentary, which is part of the National Academy of Medicine's Vital Directions for Health and Health Care: Priorities for 2021 initiative, we draw on our collective backgrounds in health financing, delivery, and innovation to offer consensus-based policy recommendations focused on health costs and financing. We organize our recommendations around five policy priorities: expanding insurance coverage, accelerating the transition to value-based care, advancing home-based care, improving the affordability of drugs and other therapeutics, and developing a high-value workforce. Within each priority we provide recommendations for key elected officials and political appointees that could be used as starting points for evidence-based policy making that supports a more effective, efficient, and equitable health system in the US.

Addressing Patient Bias and Discrimination Against Clinicians of Diverse Backgrounds.

The duty to care for all patients is central to the health professions, but what happens when clinicians encounter patients who exhibit biased or discriminatory behaviors? While significant attention has focused on addressing clinician bias toward patients, incidents of patient bias toward clinicians also occur and are difficult to navigate.Clinicians anecdotally describe their experiences with patient bias, prejudice, and discrimination as profoundly painful and degrading. Though this phenomenon has not been rigorously studied, it is not unreasonable to postulate that the moral distress caused by patient bias may ultimately contribute to clinician burnout. Because women and minority clinicians are more likely to be targets of patient bias, this may worsen existing disparities for these groups and increase their risk for burnout. Biased behavior may also affect patient outcomes.Although some degree of ignoring derogatory comments is necessary to maintain professionalism and workflow, clinicians also have the right to a workplace free of mistreatment and abuse. How should clinicians reconcile the expectation to always "put patients first" with their basic right to be treated with dignity and respect? And how can health care organizations develop policies and training to mitigate the effects of these experiences?The authors discuss the ethical dilemmas associated with responding to prejudiced patients and then present a framework for clinicians to use when directly facing or witnessing biased behavior from patients. Finally, they describe strategies to address patient bias at the institutional level.