There is no consensus on how best to assess the health literacy demands of health information materials. Comprehensive, reliable, and valid assessment tools are needed. The authors report on the development, refinement, and testing of Health Literacy INDEX, a new tool reflecting empirical evidence and best practices. INDEX is comprised of 63 indicators organized into 10 criteria: plain language, clear purpose, supporting graphics, user involvement, skill-based learning, audience appropriateness, user instruction, development details, evaluation methods, and strength of evidence. In a sample of 100 materials, intercoder agreement was high: 90% or better for 52% of indicators, and above 80% for nearly all others. Overall scores generated by INDEX were highly correlated with average ratings from 12 health literacy experts (r = 0.89, p < .0001). Additional research is warranted to examine the association between evaluation ratings generated by INDEX and individual understanding, behaviors, and improved health. Health Literacy INDEX is a comprehensive tool with evidence for reliability and validity that can be used to evaluate the health literacy demands of health information materials. Although improvement in health information materials is just one aspect of mitigating the effects of limited health literacy on health outcomes, it is an essential step toward a more health literate public.
BackgroundOn-demand telemedicine is increasingly adopted by health organizations to meet patient demand for convenient, accessible, and affordable services. Little guidance is currently available to new entrant organizations as they consider viable business models and strategies to harness the disruptive potential of on-demand telemedicine services (in particular, virtual urgent care clinics [VCCs] as a predominant and catalyst form of on-demand telemedicine).ObjectiveWe recognized on-demand telemedicine as a disruptive technology to explore the experiences of early adopter organizations as they launch on-demand telemedicine services and deploy business models and strategies. Focusing on VCC service lines, this study addressed the following research questions: (1) what is the emerging business model being deployed for on-demand telemedicine?; (2) what are the core components of the emerging business model for on-demand telemedicine?; and (3) what are the disruptive business strategies employed by early adopter organizations as they launch on-demand telemedicine services?MethodsThis qualitative study gathered data from 32 semistructured phone interviews with key informants from 19 VCC early adopter organizations across the United States. Interview protocols were developed based on noted dissemination and implementation science frameworks. We used the constant comparison method to transform study data into stable dimensions that revealed emerging business models, core business model components (value proposition, key resources, key processes, and profit formula), and accompanying business strategies.ResultsEarly adopters are deploying business models that most closely align with a value-adding process model archetype. By and large, we found that this general model appropriately matches resources, processes, and profit formulas to support the disruptive potential of on-demand telemedicine. In total, 4 business strategy areas were discovered to particularly contribute to business model success for on-demand disruption among early adopters: fundamental disruptions to the model of care delivery; outsourcing support for on-demand services; disruptive market strategies to target potential users; and new and unexpected organizational partnerships to increase return on investment.ConclusionsOn-demand telemedicine is a potentially disruptive innovation currently in the early adopter stage of technology adoption and diffusion. On-demand telemedicine must cross into the early majority stage to truly be a positive disruption that will increase accessibility and affordability for health care consumers. Our findings provide guidance for adopter organizations as they seek to deploy viable business models and successful strategies to smooth the transition to early majority status. We present important insights for both early adopters and potential early majority organizations to better harness the disruptive potential of on-demand telemedicine.
The views expressed here are solely those of the authors and do not necessarily represent the position of the Veterans Health Administration, United States government, or authors' respective academic institutions.
Background: The Veterans Community Care Program (VCCP) aims to address access constraints in the Veterans Health Administration (VA) by reimbursing care from non-VA community providers. Little existing research explores how veterans’ choice of VA versus VCCP providers has evolved as a significant VCCP expansion in 2014 as part of the Veterans Access, Choice, and Accountability Act. Objectives: We examined changes in reliance on VA for primary care (PC), mental health (MH), and specialty care (SC) among VCCP-eligible veterans. Research Design: We linked VA administrative data with VCCP claims to retrospectively examine utilization during calendar years 2016–2018. Subjects: 1.78 million veterans enrolled in VA before 2013 and VCCP-eligible in 2016 due to limited VA capacity or travel hardship. Measures: We measured reliance as the proportion of total annual outpatient (VA+VCCP) visits occurring in VA for PC, MH, and SC. Results: Of the 26.1 million total outpatient visits identified, 45.6% were for MH, 29.9% for PC, and 24.4% for SC. Over the 3 years, 83.2% of veterans used any VA services, 23.8% used any VCCP services, and 20.0% were dual VA-VCCP users. Modest but statistically significant declines in reliance were observed from 2016–2018 for PC (94.5%–92.2%), and MH (97.8%–96.9%), and a more significant decline was observed for SC (88.5%–79.8%). Conclusions: Veterans who have the option of selecting between VA or VCCP providers continued using VA for most of their outpatient care in the initial years after the 2014 VCCP expansion.
Primary care providers (PCPs), including physicians and advanced practice providers, are the front line of medical care. Patient access must balance PCP availability and patient needs. This work develops a new PCP staffing metric using panel size and full-time equivalent data to determine whether a clinic is adequately staffed and describes variation by clinic rurality. Data were from the Veterans Health Administration, 2017-2021. Results describe the gap staffing metric, provide summary graphics, and compare the gap staffing between rural and urban clinics. This novel gap staffing metric can inform strategic clinic staffing in health care systems.
This article is intended to spur inspiration for possible telehealth and community health/community benefit connections.
Context: Analyses of prescribing trends using prescription drug monitoring programs (PDMP) are impacted by changes in reporting requirements and in the scheduling of medications by the Drug Enforcement Administration. In 2014, the Drug Enforcement Administration changed the status of tramadol from an unscheduled to a scheduled medication. The addition of tramadol to the PDMP may affect the prevalence of opioid-prescribing metrics and the interpretation of prescribing trends. Objective: The objectives were to (1) examine trends in opioid prescribing in Washington State between 2012 and 2017, (2) assess the potential impact of adding tramadol to PDMP on these trends, and (3) describe challenges in defining and implementing opioid-prescribing metrics. Design: Analysis of quarterly summary statistics of opioid prescribing. Setting: Washington State. Participants: Washington State residents. Main Outcome Measures: The metrics include measures of opioid prescribing overall and by age group, chronic opioid prescribing, high-dose prescribing among those on chronic opioid therapy, prescribing of concurrent opioids and sedatives, days' supply of new opioid prescriptions, and transition from short-term to long-term use of opioids. Results: In Washington, the prevalence of any opioid prescribing, chronic opioid prescribing, high-dose opioid prescribing, and prescribing of concurrent opioids and sedatives declined between 2012 and 2017. The prevalence of opioid prescribing was higher in older than in younger age groups. The addition of tramadol to the Washington PDMP in 2014 affected the observed prevalence of all opioid metrics and of all opioid-prescribing trends. Conclusions about trends in opioid prescribing differ substantially depending on whether tramadol is included or not, particularly in 2014 and 2015. Conclusions: The development of opioid-prescribing metrics is relatively new. There is likely much benefit of standard definitions of opioid metrics at the state and national levels to track important trends and compare progress from state to state.
ObjectiveHigh-risk opioid prescribing practices in workers’ compensation (WC) settings are associated with excess opioid-related morbidity, longer work disability and higher costs. This study characterises the burden of prescription opioid-related hospitalisations among injured workers.MethodsHospital discharge data for eight states (Arizona, Colorado, Michigan, New Jersey, New York, South Carolina, Utah and Washington) were obtained from the State Inpatient Databases, Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality. We calculated 5-year (2010–2014) average annual rates of prescription opioid overdose/adverse effect (AE) hospitalisations. Injured workers were identified using payer (WC) and external cause codes.ResultsState-level average annual prescription opioid overdose/AE hospitalisation rates ranged from 0.3 to 1.2 per 100 000 employed workers. Rates for workers aged ≥65 years old were two to six times the overall rates. Among those hospitalised with prescription opioid overdose/AEs, injured workers were more likely than other inpatients to have a low back disorder diagnosis, and less likely to have an opioid dependence/abuse or cancer diagnosis, or a fatal outcome. Averaged across states, WC was the primary expected payer for <1% of prescription opioid overdose/AE hospitalisations vs 6% of injury hospitalisations.ConclusionsPopulation-based estimates of prescription opioid morbidity are almost nonexistent for injured workers; this study begins to fill that gap. Rates for injured workers increased markedly with age but were low relative to inpatients overall. Research is needed to assess whether WC as payer adequately identifies work-related opioid morbidity for surveillance purposes, and to further quantify the burden of prescription opioid-related morbidity.
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