Patterns and Influencing Factors of eHealth Tools Adoption Among Medicaid and Non-Medicaid Populations From the Health Information National Trends Survey (HINTS) 2017-2019: Questionnaire Study

Association Between the Composite Cardiovascular Risk and mHealth Use Among Adults in the 2017-2020 Health Information National Trends Survey: Cross-Sectional Study

BACKGROUND

Numerous studies have suggested that the relationship between cardiovascular disease (CVD) risk and the usage of mobile health (mHealth) technology may vary depending on the total number of CVD risk factors present. However, whether higher CVD risk is associated with a greater likelihood of engaging in specific mHealth use among US adults is currently unknown.

OBJECTIVE

We aim to assess the associations between the composite CVD risk and each component of mHealth use among US adults regardless of whether they have a history of CVD or not.

METHODS

This study used cross-sectional data from the 2017 to 2020 Health Information National Trends Survey. The exposure was CVD risk (diabetes, hypertension, smoking, physical inactivity, and overweight or obesity). We defined low, moderate, and high CVD risk as having 0-1, 2-3, and 4-5 CVD risk factors, respectively. The outcome variables of interest were each component of mHealth use, including using mHealth to make health decisions, track health progress, share health information, and discuss health decisions with health providers. We used multivariable logistic regression models to examine the association between CVD risk and mHealth use adjusted for demographic factors.

RESULTS

We included 10,531 adults, with a mean age of 54 (SD 16.2) years. Among the included participants, 50.2% were men, 65.4% were non-Hispanic White, 41.9% used mHealth to make health decisions, 50.8% used mHealth to track health progress toward a health-related goal, 18.3% used mHealth to share health information with health providers, and 37.7% used mHealth to discuss health decisions with health providers (all are weighted percentages). Adults with moderate CVD risk were more likely to use mHealth to share health information with health providers (adjusted odds ratio 1.49, 95% CI 1.24-1.80) and discuss health decisions with health providers (1.22, 95% CI 1.04-1.44) compared to those with low CVD risk. Similarly, having high CVD risk was associated with higher odds of using mHealth to share health information with health providers (2.61, 95% CI 1.93-3.54) and discuss health decisions with health providers (1.56, 95% CI 1.17-2.10) compared to those with low CVD risk. Upon stratifying by age and gender, we observed age and gender disparities in the relationship between CVD risk and the usage of mHealth to discuss health decisions with health providers.

CONCLUSIONS

Adults with a greater number of CVD risk factors were more likely to use mHealth to share health information with health providers and discuss health decisions with health providers. These findings suggest a promising avenue for enhancing health care communication and advancing both primary and secondary prevention efforts related to managing CVD risk factors through the effective usage of mHealth technology.

Descriptive Norms and eHealth Use Among Older Adults: A Cross-Country Comparative Study

eHealth use enables older adults to access and manage healthcare resources, and benefits their health; however, older adults' uptake of eHealth remains low across societies. Social influences such as descriptive norms may be of critical importance in promoting the elderly's usage of eHealth. Based on the Integrative Model of Behavioral Prediction, this study investigates how descriptive norms relate to eHealth use among the elderly in China and the United States. Analysis of the combined sample (N = 1,070) showed that descriptive norms were positively related to eHealth use. Also, descriptive norms were indirectly associated with eHealth use via injunctive norms, attitudes and self-efficacy. Moderated mediation analysis indicated that these direct and indirect relationships differed across the two countries. This study highlights the important role of descriptive norms in promoting older adults' eHealth use behavior and addresses potential country differences in how the elderly respond to descriptive norms. Several important theoretical and practical implications are also discussed.

Examining the Intervening Roles of Patient-Centered Care and Patient Activation in the Health Impacts of Offline Healthcare Obstacles and Online Health Consultations Among Deaf and Hard-of-Hearing Patients

Deaf and hard-of-hearing (DHH) patients often encounter difficulties in effective communication with healthcare professionals and are less likely to receive quality medical care. However, DHH populations are understudied in health communication research. This study examined how offline healthcare obstacles and online health consultation impact DHH patients' health, and the mediating roles of patient-centered care (PCC) and patient activation. Data from 323 DHH patients were analyzed using structural equation modeling to test the hypothesized mediation pathway model. Results indicate that offline healthcare obstacles negatively affect DHH patients' perception of patient-centeredness, which reduces their ability and confidence in self-care (conceptualized as patient activation in this study). This reduced patient activation may jeopardize DHH patients' physical and psychological health. Meanwhile, online health consultation is positively associated with PCC, and higher levels of PCC can increase patient activation, contributing to better physical and psychological health. Testing the same model with hearing-abled participants (n = 3542) revealed significant differences in these intervening relationships. Overall, this study provides valuable insights into the relationship between DHH patients' healthcare experience and their health outcomes. The findings support interventions that focus on enhancing PCC and patient activation to improve the physical and psychological health outcomes of DHH patients.

Retrospective assessment of a collaborative digital asthma program for Medicaid-enrolled children in southwest Detroit: reductions in short-acting beta-agonist (SABA) medication use

BACKGROUND

Real-world evidence for digitally-supported asthma programs among Medicaid-enrolled children remains limited. Using data from a collaborative quality improvement program, we evaluated the impact of a digital intervention on asthma inhaler use among children in southwest Detroit.

METHODS

Children (6-13 years) enrolled with Kids Health Connection (KHC), a program involving home visits with an asthma educator, were invited to participate in a digital self-management asthma program (Propeller Health). Patients were provided with a sensor to capture short-acting beta-agonist (SABA) medication use, and given access to a paired mobile app to track usage. Patients' healthcare providers and caregivers ("followers") were invited to view data as well. Retrospective paired t-tests assessed change in mean SABA use and SABA-free days (SFD) over time, and regressions explored the relationship between followers and medication use.

RESULTS

Fifty-one patients were assessed. Mean program participation was nine months, and patients had on average 3 followers. From the first to last participation month, mean SABA use decreased from 0.68 to 0.25 puffs/day (p < 0.001), and mean SFD increased from 25.2 to 28.1 days/month (p < 0.001). 76% of patients had an increase in the number of SFD. There was a positive, but non-significant, relationship between the number of followers and reductions in SABA inhaler use.

CONCLUSIONS

We observed a significant reduction in SABA inhaler use and an increase in the number of SABA-free days among Medicaid-enrolled children enrolled in a multi-modal digital asthma program.

Feasibility and Acceptability of a Telemedicine Triage Model Among Medicaid Patients with Low Back Pain Referred to a Spine Center

Background

The data for primary triage via telemedicine for spine related conditions are sparse but has potential to improve access, quality of care, and offer significant cost savings for Medicaid insured patients who have very limited access to care. The purpose of this study was to evaluate the feasibility and acceptability of implementing a telehealth triage framework using synchronous video conferencing appointments.

Methods

This is a prospective cohort feasibility study conducted within an academic spine center in the United States. Participants include Medicaid insured patients referred to an academic spine center for low back pain. We collected demographic information, a spine red flag survey, a patient satisfaction survey and demand and implementation feasibility metrics. Participants completed a demographic and red-flag survey followed by a telehealth spine appointment with a physiatrist. Immediately after the appointment, the participant completed a satisfaction survey.

Results

Nineteen patients met inclusion criteria but declined telehealth either due to preference for in-person appointment or lack of comfort with technology. Thirty-three participants enrolled and attended their initial telehealth appointment. Few participants reporting 1 or more red flag symptom also screened positive during their subsequent telehealth evaluation with the physician (n=7/28). Participant satisfaction was high across all domains including ease of scheduling, efficiency of virtual check in, ability to report their symptoms fully and accurately to the provider, imaging review, explanation of diagnosis and treatment plan. Most participants (n=19/20, 95%) would recommend an initial telehealth appointment.

Conclusions

The telehealth framework used was feasible and provided an acceptable form of care for Medicaid patients who were interested and able to participate in this form of care. Our acceptability results are promising but should be interpreted with caution given the proportion of patients who declined to participate.

The Disparities in Patient Portal Use Among Patients With Rheumatic and Musculoskeletal Diseases: Retrospective Cross-sectional Study

BACKGROUND

During the COVID-19 pandemic, the shift to virtual care became essential for the continued care of patients. Individuals with rheumatic and musculoskeletal diseases (RMDs) especially require frequent provider visits and close monitoring. To date, there have been limited studies examining inequities in health technology use among patients with RMDs.

OBJECTIVE

Our goal was to identify characteristics associated with patient portal use before and after the COVID-19 pandemic in a convenience sample of patients with RMDs from a large academic medical center.

METHODS

In this cross-sectional study, Epic electronic medical record data were queried to identify established patients of the University of North Carolina Hospitals adult rheumatology clinic between November 1, 2017, through November 30, 2019. Demographic and clinical data were collected to compare MyChart (Epic's patient portal) users with nonusers before and after the COVID-19 pandemic. MyChart activation and use were modeled using logistic regression and adjusted odds ratios, and confidence intervals were estimated.

RESULTS

We identified 5075 established patients with RMDs who met the inclusion criteria. Prior to the pandemic, we found that younger age (P<.001), suburban residence (P=.05), commercial/state insurance (P<.001), military insurance (P=.05), and median income >US $50,000 (P<.001) were associated with significantly higher odds of MyChart activation. Male sex (P<.001), being of Black or African American (P<.001) or "other" race (P<.001), Spanish as a primary language (P<.001), rural residence (P=.007), Medicaid insurance (P<.001), and median income of <US $25,000 (P=.01) were associated with lower odds of MyChart activation. Following COVID-19, younger age (P<.001), commercial insurance (P=.03), state insurance (P=.02), and median income of US $50,000-75,000 (P=.01) were associated with significantly higher odds of MyChart use. However, being of Black or African American (P<.001) or "other" race (P=.01), Spanish as a primary language (P=.002), male sex (P=.004), rural residence (P=.005), and having no insurance (P<.001) or Medicaid (P=.008) were associated with lower odds of MyChart use.

CONCLUSIONS

Residence in a rural area, being of minority race/ethnicity, older age, male sex, lower median income, Medicaid, being uninsured, and non-English primary language are associated with lower odds of patient portal activation and use. Future health policy and clinical practice measures should focus on reducing barriers to health technology adoption among these groups.

Impact of Hospital Characteristics and Governance Structure on the Adoption of Tracking Technologies for Clinical and Supply Chain Use: Longitudinal Study of US Hospitals

Background

Despite the increasing adoption rate of tracking technologies in hospitals in the United States, few empirical studies have examined the factors involved in such adoption within different use contexts (eg, clinical and supply chain use contexts). To date, no study has systematically examined how governance structures impact technology adoption in different use contexts in hospitals. Given that the hospital governance structure fundamentally governs health care workflows and operations, understanding its critical role provides a solid foundation from which to explore factors involved in the adoption of tracking technologies in hospitals.

Objective

This study aims to compare critical factors associated with the adoption of tracking technologies for clinical and supply chain uses and examine how governance structure types affect the adoption of tracking technologies in hospitals.

Methods

This study was conducted based on a comprehensive and longitudinal national census data set comprising 3623 unique hospitals across 50 states in the United States from 2012 to 2015. Using mixed effects population logistic regression models to account for the effects within and between hospitals, we captured and examined the effects of hospital characteristics, locations, and governance structure on adjustments to the innate development of tracking technology over time.

Results

From 2012 to 2015, we discovered that the proportion of hospitals in which tracking technologies were fully implemented for clinical use increased from 36.34% (782/2152) to 54.63% (1316/2409), and that for supply chain use increased from 28.58% (615/2152) to 41.3% (995/2409). We also discovered that adoption factors impact the clinical and supply chain use contexts differently. In the clinical use context, compared with hospitals located in urban areas, hospitals in rural areas (odds ratio [OR] 0.68, 95% CI 0.56-0.80) are less likely to fully adopt tracking technologies. In the context of supply chain use, the type of governance structure influences tracking technology adoption. Compared with hospitals not affiliated with a health system, implementation rates increased as hospitals affiliated with a more centralized health system—1.9-fold increase (OR 1.87, 95% CI 1.60-2.13) for decentralized or independent hospitals, 2.4-fold increase (OR 2.40, 95% CI 2.07-2.80) for moderately centralized health systems, and 3.1-fold increase for centralized health systems (OR 3.07, 95% CI 2.67-3.53).

Conclusions

As the first of such type of studies, we provided a longitudinal overview of how hospital characteristics and governance structure jointly affect adoption rates of tracking technology in both clinical and supply chain use contexts, which is essential for developing intelligent infrastructure for smart hospital systems. This study informs researchers, health care providers, and policy makers that hospital characteristics, locations, and governance structures have different impacts on the adoption of tracking technologies for clinical and supply chain use and on health resource disparities among hospitals of different sizes, locations, and governance structures.

Sociodemographic factors affecting telemedicine access: A population-based analysis

Background

The coronavirus disease 2019 pandemic prompted a surge in telemedicine, with the presumption that patients had computer and internet access. We sought to determine, in a population-based sample, how many Americans were using computers and the internet before the pandemic, and whether disparities existed in this.

Methods

The National Health Interview Survey is conducted annually by the Centers for Disease Control and Prevention and is representative of the entire United States civilian non-institutionalized population. In 2018, questions were fielded regarding computer and web utilization. We evaluated sociodemographic factors associated with this.

Results

Twenty-five thousand and forty-nine people, representing 245,842,992 in the population, responded to these questions. Of the responses, 19% stated they used a computer “never or almost never,” 18% stated they did not use the internet, and 25% did not use email. Over the previous 12 months, 55% of responders stated they had looked up health information on the internet, 11% had filled a prescription online, 16% had scheduled a medical appointment on the internet, and 17% had communicated with a healthcare provider by email. Internet usage varied by region, age, race, education, family income, and insurance status, but not by gender.

Conclusion

As telemedicine becomes more prevalent, sociodemographic factors limiting computer and internet use may disadvantage certain segments of the population.