Objectives. To quantify the number of people in the US who delay medical care annually because of lack of available transportation and to examine the differential prevalence of this barrier for adults across sociodemographic characteristics and patient populations.Methods. We used data from the National Health Interview Survey (1997-2017) to examine this barrier over time and across groups. We used joinpoint regression analysis to identify significant changes in trends and multivariate analysis to examine correlates of this barrier for the year 2017.Results. In 2017, 5.8 million persons in the United States (1.8%) delayed medical care because they did not have transportation. The proportion reporting transportation barriers increased between 2003 and 2009 with no significant trends before or after this window within our study period. We found that Hispanic people, those living below the poverty threshold, Medicaid recipients, and people with a functional limitation had greater odds of reporting a transportation barrier after we controlled for other sociodemographic and health characteristics.Conclusions. Transportation barriers to health care have a disproportionate impact on individuals who are poor and who have chronic conditions. Our study documents a significant problem in access to health care during a time of rapidly changing transportation technology.

Transportation Barriers to Health Care in the United States: Findings From the National Health Interview Survey, 1997-2017.

Opioid-related deaths have risen dramatically in rural communities. Prior studies highlight few medication treatment providers for opioid use disorder in rural communities, though literature has ye...

https://www.tandfonline.com/doi/pdf/10.1080/00952990.2019.1694536

A systematic review of rural-specific barriers to medication treatment for opioid use disorder in the United States

Report to Congress: Social Risk Factors and Performance Under Medicare’s Value-based Purchasing Programs.1

US Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation.

December 21, 2016.

### Policy Context

Value-based purchasing (VBP), or pay-for-performance, comprises a growing portion of Medicare payment and the changes in physician payment enacted in the Medicare and Children's Health Insurance Program Reauthorization Act are likely to accelerate these trends even further.2 Simultaneously, there is growing consensus that social risk factors—such as income, race and ethnicity, and community environment—play a major role in health.3–5 Persistent and meaningful gaps exist in health and even in life expectancy based on these factors.6–9

These 2 issues intersect in VBP. If beneficiaries with social risk factors have worse health outcomes because of factors beyond providers’ control, providers could be unfairly disadvantaged under VBP. On the contrary, if beneficiaries with social risk factors have worse health outcomes because the providers serving them provide low-quality care, the financial incentives and accountability of VBP could be an important strategy for improving care and reducing disparities.

In 2014, Congress passed the Improving Medicare Postacute Care Transformation Act (IMPACT),10 which required that the Office of the Assistant Secretary for Planning and Evaluation (ASPE) at the US Department of Health and Human Services complete an empirical report addressing the issue of social risk in Medicare’s current VBP programs to assist Congress in further decision making on this issue. The report covered here is the first component of the required work, which was submitted to Congress in December 2016.

The ASPE report may have particular salience to cardiovascular clinicians, who practice every day in an environment significantly impacted by VBP. Many current quality measures in inpatient and outpatient VBP programs are cardiovascular in nature; patients …

Social Risk Factors and Performance Under Medicare's Value-Based Purchasing Programs.

Importance Evidence-based guidance is limited on how clinicians should screen for social risk factors and which interventions related to these risk factors improve health outcomes. Objective To describe research on screening and interventions for social risk factors to inform US Preventive Services Task Force considerations of the implications for its portfolio of recommendations. Data Sources Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Ovid MEDLINE, Sociological Abstracts, and Social Services Abstracts (through 2018); Social Interventions Research and Evaluation Network evidence library (January 2019 through May 2021); surveillance through May 21, 2021; interviews with 17 key informants. Study Selection Individual-level and health care system–level interventions with a link to the health care system that addressed at least 1 of 7 social risk domains: housing instability, food insecurity, transportation difficulties, utility needs, interpersonal safety, education, and financial strain. Data Extraction and Synthesis One investigator abstracted data from studies and a second investigator evaluated data abstractions for completeness and accuracy; key informant interviews were recorded, transcribed, summarized, and integrated with evidence from the literature; narrative synthesis with supporting tables and figures. Main Outcomes and Measures Validity of multidomain social risk screening tools; all outcomes reported for social risk–related interventions; challenges or unintended consequences of screening and interventions. Results Many multidomain social risk screening tools have been developed, but they vary widely in their assessment of social risk and few have been validated. This technical brief identified 106 social risk intervention studies (N = 5 978 596). Of the interventions studied, 73 (69%; n = 127 598) addressed multiple social risk domains. The most frequently addressed domains were food insecurity (67/106 studies [63%], n = 141 797), financial strain (52/106 studies [49%], n = 111 962), and housing instability (63/106 studies [59%], n = 5 881 222). Food insecurity, housing instability, and transportation difficulties were identified by key informants as the most important social risk factors to identify in health care. Thirty-eight studies (36%, n = 5 850 669) used an observational design with no comparator, and 19 studies (18%, n = 15 205) were randomized clinical trials. Health care utilization measures were the most commonly reported outcomes in the 68 studies with a comparator (38 studies [56%], n = 111 102). The literature and key informants described many perceived or potential challenges to implementation of social risk screening and interventions in health care. Conclusions and Relevance Many interventions to address food insecurity, financial strain, and housing instability have been studied, but more randomized clinical trials that report health outcomes from social risk screening and intervention are needed to guide widespread implementation in health care.

/pdf/screening-and-interventions-for-social-risk-factors-4zvyo7pucs.pdf

Screening and Interventions for Social Risk Factors: Technical Brief to Support the US Preventive Services Task Force

INTRODUCTION Lack of reliable transportation can be a barrier to keeping appointments or accessing other health care services. Increasingly, insurers and health care delivery systems subsidize transportation services for patients. This systematic review synthesizes existing research on nonemergency medical transportation interventions. METHODS We searched 3 databases (Embase, PubMed, Google) for studies of health care sector-sponsored programs that provided patients assistance with nonemergency transportation and directly assessed the impact of transportation assistance on health and health care utilization outcomes. Studies meeting inclusion criteria were graded for quality using standard grading criteria. FINDINGS Eight studies met all inclusion criteria. Most were rated as low quality. All studies included examined process or health care utilization outcomes, such as uptake of transportation services, return for follow-up, or missed appointment rates; only 1 included health outcomes, such as illness severity and blood pressure. Results were mixed. More rigorous studies showed low patient uptake of transportation services and inconsistent impacts on health and utilization outcomes. CONCLUSIONS Despite considerable interest in subsidizing transportation services to improve health for patients facing transportation barriers, little rigorously conducted research has demonstrated the impact of transportation services on health or health care utilization. Some extant literature suggests that transportation assistance is more likely to be effective when offered with other interventions to reduce social and economic barriers to health.

Impact of transportation interventions on health care outcomes: a systematic review

Importance Transportation barriers contribute to missed primary care appointments for patients with Medicaid. Rideshare services have been proposed as alternatives to nonemergency medical transportation programs because of convenience and lower costs. Objective To evaluate the association between rideshare-based medical transportation and missed primary care appointments among Medicaid patients. Design, Setting, and Participants In a prospective clinical trial, 786 Medicaid beneficiaries who resided in West Philadelphia and were established primary care patients at 1 of 2 academic internal medicine practices located within the same building were included. Participants were allocated to being offered complimentary ride-sharing services (intervention arm) or usual care (control arm) based on the prescheduled day of their primary care appointment reminder. Those scheduled on even-numbered weekdays were in the intervention arm and on odd-numbered weekdays, the control arm. The primary study outcome was the rate of missed appointments, estimated using an intent-to-treat approach. All individuals receiving a phone call reminder were included in the study sample, regardless of whether they answered their phone. The study was conducted between October 24, 2016, and April 20, 2017. Interventions A model of providing rideshare-based transportation was designed. As part of usual care, patients assigned to both arms received automated appointment phone call reminders. As part of the study protocol, patients assigned to both arms received up to 3 additional appointment reminder phone calls from research staff 2 days before their scheduled appointment. During these calls, patients in the intervention arm were offered a complimentary ridesharing service. Research staff prescheduled rides for those interested in the service. After their appointment, patients phoned research staff to initiate a return trip home. Main Outcomes and Measures Missed appointment rate (no shows and same-day cancellations) in the intervention compared with control arm. Results Of the 786 patients allocated to the intervention or control arm, 566 (72.0%) were women; mean (SD) age was 46.0. (12.5) years. Within the intervention arm, 57 among 288 (19.8%) participants who answered the phone call used ridesharing. The missed appointment rate was 36.5% (144 of 394) for the intervention arm and 36.7% (144 of 392) for the control arm ( P  = .96). Conclusions and Relevance The uptake of ridesharing was low and did not decrease missed primary care appointments. Future studies trying to reduce missed appointments should explore alternative delivery models or targeting populations with stronger transportation needs. Trial Registration clinicaltrials.gov Identifier:NCT02955433

/pdf/association-of-rideshare-based-transportation-services-and-1uo6a5q5p7.pdf

Association of Rideshare-Based Transportation Services and Missed Primary Care Appointments: A Clinical Trial

SummaryAutomated chatbots offer the promise of reducing reliance on personnel while maintaining or increasing effective and comfortable customer engagement. But accuracy standards are higher and quality assurance more difficult in health care settings, where patients are the customers. The University of Pennsylvania Health System implemented a prototype chatbot to reduce message burden on clinicians in a text-based remote hypertension management program. The chatbot accurately triaged 99% of patient messages (1,379 of 1,393), with most messages, nearly 75% (1,073), not requiring escalation to the clinician. The authors share both their approach for building while doing using innovation methodology and the lessons learned in developing a chatbot for clinical care. 

Developing a Chatbot–Clinician Model for Hypertension Management

https://doi.org/10.1016/j.hjdsi.2022.100614

An innovation tournament to improve medical residency

Background Free-text communication between patients and providers plays an increasing role in chronic disease management, through platforms varying from traditional health care portals to novel mobile messaging apps. These text data are rich resources for clinical purposes, but their sheer volume render them difficult to manage. Even automated approaches, such as natural language processing, require labor-intensive manual classification for developing training data sets. Automated approaches to organizing free-text data are necessary to facilitate use of free-text communication for clinical care. Objective The aim of this study was to apply unsupervised learning approaches to (1) understand the types of topics discussed and (2) learn medication-related intents from messages sent between patients and providers through a bidirectional text messaging system for managing participant blood pressure (BP). Methods This study was a secondary analysis of deidentified messages from a remote, mobile, text-based employee hypertension management program at an academic institution. We trained a latent Dirichlet allocation (LDA) model for each message type (ie, inbound patient messages and outbound provider messages) and identified the distribution of major topics and significant topics (probability >.20) across message types. Next, we annotated all medication-related messages with a single medication intent. Then, we trained a second medication-specific LDA (medLDA) model to assess how well the unsupervised method could identify more fine-grained medication intents. We encoded each medication message with n-grams (n=1-3 words) using spaCy, clinical named entities using Stanza, and medication categories using MedEx; we then applied chi-square feature selection to learn the most informative features associated with each medication intent. Results In total, 253 participants and 5 providers engaged in the program, generating 12,131 total messages: 46.90% (n=5689) patient messages and 53.10% (n=6442) provider messages. Most patient messages corresponded to BP reporting, BP encouragement, and appointment scheduling; most provider messages corresponded to BP reporting, medication adherence, and confirmatory statements. Most patient and provider messages contained 1 topic and few contained more than 3 topics identified using LDA. In total, 534 medication messages were annotated with a single medication intent. Of these, 282 (52.8%) were patient medication messages: most referred to the medication request intent (n=134, 47.5%). Most of the 252 (47.2%) provider medication messages referred to the medication question intent (n=173, 68.7%). Although the medLDA model could identify a majority intent within each topic, it could not distinguish medication intents with low prevalence within patient or provider messages. Richer feature engineering identified informative lexical-semantic patterns associated with each medication intent class. Conclusions LDA can be an effective method for generating subgroups of messages with similar term usage and facilitating the review of topics to inform annotations. However, few training cases and shared vocabulary between intents precludes the use of LDA for fully automated, deep, medication intent classification. International Registered Report Identifier (IRRID) RR2-10.1101/2021.12.23.21268061

Identifying Medication-Related Intents From a Bidirectional Text Messaging Platform for Hypertension Management Using an Unsupervised Learning Approach: Retrospective Observational Pilot Study

Elizabeth Asch

Papers

Association of Rideshare-Based Transportation Services and Missed Primary Care Appointments: A Clinical Trial

Developing a Chatbot–Clinician Model for Hypertension Management

An innovation tournament to improve medical residency

Identifying Medication-Related Intents From a Bidirectional Text Messaging Platform for Hypertension Management Using an Unsupervised Learning Approach: Retrospective Observational Pilot Study