A Hexagonal Aim as a Driver of Change for Health Care and Health Insurance Systems.

Policy Points Improving health systems requires simultaneous pursuit of a patient centered approach aligned with the health professional: improving the experience of care, improving the health of populations, reducing per capita costs of care - Triple Aim - and improving the work life of the care providers - Quadruple Aim -. Reinforcing the recently defined Fifth Aim as equity through "health democracy" to represent the wants, needs and responsibility of the population in taking care of their health and their healthcare. Adding a Sixth Aim to take into account the increased health risks due to climate change. CONTEXT: Improving health systems, such as the U.S. or French, requires simultaneous pursuit of a patient centered approach aligned with the health professional: improving the experience of care, improving the health of populations, reducing per capita costs of care - Triple Aim - and improving the work life of the care providers, including clinicians and staff - Quadruple Aim -. While these aims are already ambitious, they may be insufficient when considering the economic, social and environmental challenges to the health of our communities in the near and long term. METHODS: A conceptual framework to provide additional ethical guardrails for health systems. RESULTS: Recently, authors have articulated a Fifth Aim and we propose to add a Sixth Aim to the Quadruple Aim model. These additional aims are meant to account for our growing knowledge around the determinants of health and the challenging processes and structures of governance across a wide range of sectors in society including healthcare. We are strengthening the Fifth Aim defined as equity through "health democracy" to represent the wants, needs and responsibility of the population in taking care of their health and their healthcare. The Sixth Aim is to account for the increase in risk to population health due to climate change as well as the impact our health systems have on the environment. CONCLUSIONS: As social tension and environmental changes seem to continue to impact the structure of our society this "Hexagonal Aim" taken together might provide additional ethical guiderails as we set our healthcare goals.

Predictors of COVID-19 vaccine uptake: an online three-wave survey study of US adults.

BACKGROUND: To effectively promote vaccine uptake, it is important to understand which people are most and least inclined to be vaccinated and why. In this study, we examined predictors of COVID-19 vaccine uptake and reasons for non-vaccination. METHODS: We conducted an online English-language survey study in December-2020, January-2021, and March-2021. A total of 930 US respondents completed all surveys. Multiple logistic regression models were run to test whether the early vaccine eligibility, demographic factors, and psychological factors predict getting at least one dose of a COVID-19 vaccination in January-2021 and in March-2021. RESULTS: The proportion of respondents who received ≥ 1-dose of a COVID-19 vaccine increased from 18% (January) to 67% (March). Older age predicted vaccine uptake in January (OR = 2.02[95%CI = 1.14-3.78], p < .001) and March (10.92[6.76-18.05], p < .001). In January, additional predictors were higher numeracy (1.48[1.20-1.86], p < .001), COVID-19 risk perceptions (1.35[1.03-1.78], p = .029), and believing it is important adults get the COVID-19 vaccine (1.66[1.05-2.66], p = .033). In March, additional predictors of uptake were believing it is important adults get the COVID-19 vaccine (1.63[1.15-2.34], p = .006), prior COVID-19 vaccine intentions (1.37[1.10-1.72], p = .006), and belief in science (0.84[0.72-0.99], p = .041). Concerns about side effects and the development process were the most common reasons for non-vaccination. Unvaccinated respondents with no interest in getting a COVID-19 vaccine were younger (0.27[0.09-0.77], p = .016), held negative views about COVID-19 vaccines for adults (0.15[0.08-0.26], p < .001), had lower trust in healthcare (0.59[0.36-0.95], p = .032), and preferred to watch and wait in clinically ambiguous medical situations (0.66[0.48-0.89], p = .007). CONCLUSIONS: Evidence that attitudes and intentions towards COVID-19 vaccines were important predictors of uptake provides validation for studies using these measures and reinforces the need to develop strategies for addressing safety and development concerns which remain at the forefront of vaccine hesitancy.

Patient Perspectives on a Patient-Facing Tool for Lung Cancer Screening.

BACKGROUND: Individuals with high risk for lung cancer may benefit from lung cancer screening, but there are associated risks as well as benefits. Shared decision-making (SDM) tools with personalized information may provide key support for patients. Understanding patient perspectives on educational tools to facilitate SDM for lung cancer screening may support tool development. AIM: This study aimed to explore patient perspectives related to a SDM tool for lung cancer screening using a qualitative approach. METHODS: We elicited patient perspectives by showing a provider-facing SDM tool. Focus group interviews that ranged in duration from 1.5 to 2 h were conducted with 23 individuals with high risk for lung cancer. Data were interpreted inductively using thematic analysis to identify patients' thoughts on and desires for a patient-facing SDM tool. RESULTS: The findings highlight that patients would like to have educational information related to lung cancer screening. We identified several key themes to be considered in the future development of patient-facing tools: barriers to acceptance, preference against screening and seeking empowerment. One further theme illustrated effects of patient-provider relationship as a limitation to meeting lung cancer screening information needs. Participants also noted several suggestions for the design of technology decision aids. CONCLUSION: These findings suggest that patients desire additional information on lung cancer screening in advance of clinical visits. However, there are several issues that must be considered in the design and development of technology to meet the information needs of patients for lung cancer screening decisions. PATIENT OR PUBLIC CONTRIBUTION: Patients, service users, caregivers or members of the public were not involved in the study design, conduct, analysis or interpretation of the data. However, clinical experts in health communication provided detailed feedback on the study protocol, including the focus group approach. The study findings contribute to a better understanding of patient expectations for lung cancer screening decisions and may inform future development of tools for SDM.

Prediction-Augmented Shared Decision-Making and Lung Cancer Screening Uptake.

Importance: Addressing poor uptake of low-dose computed tomography lung cancer screening (LCS) is critical, especially for those having the most to gain-high-benefit persons with high lung cancer risk and life expectancy more than 10 years. Objective: To assess the association between LCS uptake and implementing a prediction-augmented shared decision-making (SDM) tool, which enables clinicians to identify persons predicted to be at high benefit and encourage LCS more strongly for these persons. Design, Setting, and Participants: Quality improvement interrupted time series study at 6 Veterans Affairs sites that used a standard set of clinical reminders to prompt primary care clinicians and screening coordinators to engage in SDM for LCS-eligible persons. Participants were persons without a history of LCS who met LCS eligibility criteria at the time (aged 55-80 years, smoked ≥30 pack-years, and current smoking or quit <15 years ago) and were not documented to be an inappropriate candidate for LCS by a clinician during October 2017 through September 2019. Data were analyzed from September to November 2023. Exposure: Decision support tool augmented by a prediction model that helps clinicians personalize SDM for LCS, tailoring the strength of screening encouragement according to predicted benefit. Main outcome and measure: LCS uptake. Results: In a cohort of 9904 individuals, the median (IQR) age was 64 (57-69) years; 9277 (94%) were male, 1537 (16%) were Black, 8159 (82%) were White, 5153 (52%) were predicted to be at intermediate (preference-sensitive) benefit and 4751 (48%) at high benefit, and 1084 (11%) received screening during the study period. Following implementation of the tool, higher rates of LCS uptake were observed overall along with an increase in benefit-based LCS uptake (higher screening uptake among persons anticipated to be at high benefit compared with those at intermediate benefit; primary analysis). Mean (SD) predicted probability of getting screened for a high-benefit person was 24.8% (15.5%) vs 15.8% (11.8%) for a person at intermediate benefit (mean absolute difference 9.0 percentage points; 95% CI, 1.6%-16.5%). Conclusions and Relevance: Implementing a robust approach to personalized LCS, which integrates SDM, and a decision support tool augmented by a prediction model, are associated with improved uptake of LCS and may be particularly important for those most likely to benefit. These findings are timely given the ongoing poor rates of LCS uptake.

Evaluating a novel online behavioural intervention to encourage cost-conscious strategies among US adults with chronic conditions who are enrolled in a high-deductible health plan: a proof-of-concept pilot study.

OBJECTIVES: Patients with chronic conditions enrolled in high-deductible health plans (HDHPs) face cost-related access barriers and high out-of-pocket spending. Our objectives were to develop a novel behavioural intervention to help HDHP enrollees with chronic conditions use cost-conscious strategies and evaluate the intervention's preliminary effectiveness, acceptability and feasibility. DESIGN: Prospective. SETTING: Online (USA). PARTICIPANTS: 36 US adults enrolled in an HDHP through their employer or an exchange with diabetes, hypertension, asthma, coronary artery disease and/or chronic obstructive pulmonary disease. 31/36 participants completed the study. INTERVENTION: We developed a 5-week intervention consisting of a website with educational modules on discussing costs with clinicians, saving for future healthcare costs, comparing healthcare prices and quality, preparing for appointments, following up after appointments and planning for future healthcare needs; and emails encouraging participants to access each module. OUTCOMES: We conducted a single-arm proof-of-concept pilot study of the intervention. Baseline and postintervention surveys measured primary outcomes of health insurance literacy and confidence in using cost-conscious strategies. 10 participants completed postintervention interviews. RESULTS: 31 (86%) participants completed a baseline and postintervention survey. Mean health insurance literacy scores (20-80 scale) improved from 56.5 to 67.1 (p<0.001). Mean confidence scores (0-10 scale) improved for talking to a healthcare provider about cost (6.1-7.6, p=0.0094), saving for healthcare (5.8-6.6, p=0.068), comparing prices (5.4-6.9, p=0.005) and comparing quality (6.1 to 7.6, p=0.0034). Participants found the website easy to use and helpful for learning about cost-conscious strategies on postintervention interviews. CONCLUSIONS: Our novel behavioural intervention was acceptable to HDHP enrollees with chronic conditions, feasible to deliver and associated with increased health insurance literacy and confidence in using cost-conscious strategies. This intervention should be tested in a definitive randomised controlled trial that is fully powered to evaluate its effects on cost-related access barriers, out-of-pocket spending and health outcomes in this growing patient population.

Clinician Perceptions on Using Decision Tools to Support Prediction-Based Shared Decision Making for Lung Cancer Screening.

Background: Considering a patient's full risk factor profile can promote personalized shared decision making (SDM). One way to accomplish this is through encounter tools that incorporate prediction models, but little is known about clinicians' perceptions of the feasibility of using these tools in practice. We examined how clinicians react to using one such encounter tool for personalizing SDM about lung cancer screening (LCS). Design: We conducted a qualitative study based on field notes from academic detailing visits during a multisite quality improvement program. The detailer engaged one-on-one with 96 primary care clinicians across multiple Veterans Affairs sites (7 medical centers and 6 outlying clinics) to get feedback on 1) the rationale for prediction-based LCS and 2) how to use the DecisionPrecision (DP) encounter tool with eligible patients to personalize LCS discussions. Results: Thematic content analysis from detailing visit data identified 6 categories of clinician willingness to use the DP tool to personalize SDM for LCS (adoption potential), varying from "Enthusiastic Potential Adopter" (n = 18) to "Definite Non-Adopter" (n = 16). Many clinicians (n = 52) articulated how they found the concept of prediction-based SDM highly appealing. However, to varying degrees, nearly all clinicians identified challenges to incorporating such an approach in routine practice. Limitations: The results are based on the clinician's initial reactions rather than longitudinal experience. Conclusions: While many primary care clinicians saw real value in using prediction to personalize LCS decisions, more support is needed to overcome barriers to using encounter tools in practice. Based on these findings, we propose several strategies that may facilitate the adoption of prediction-based SDM in contexts such as LCS. Highlights: Encounter tools that incorporate prediction models promote personalized shared decision making (SDM), but little is known about clinicians' perceptions of the feasibility of using these tools in practice.We examined how clinicians react to using one such encounter tool for personalizing SDM about lung cancer screening (LCS).While many clinicians found the concept of prediction-based SDM highly appealing, nearly all clinicians identified challenges to incorporating such an approach in routine practice.We propose several strategies to overcome adoption barriers and facilitate the use of prediction-based SDM in contexts such as LCS.

Information and resources VA health system leaders need to manage enrollment and retention for Post-9/11 veterans.

OBJECTIVE: To understand Veterans Health Administration (VA) leaders' information and resource needs for managing post-9/11 Veterans' VA enrollment and retention. DATA SOURCES AND STUDY SETTING: Interviews conducted from March-May 2022 of VA Medical Center (VAMC) leaders (N = 27) across 15 sites, using stratified sampling based on VAMC characteristics: enrollment rates, number of recently separated Veterans in catchment area, and state Medicaid expansion status. STUDY DESIGN: Interview questions were developed using Petersen et al.'s Factors Influencing Choice of Healthcare System framework as a guide. Interviews were transcribed verbatim, and two coders analyzed the interviews using Atlas.ti, a qualitative software program. Coders followed the qualitative coding philosophy developed by Crabtree and Miller, a process of developing codes for salient concepts as they are identified during the analysis process. DATA COLLECTION/EXTRACTION METHODS: Two coders analyzed 22% (N = 6) of the interviews and discussed and adjudicated any discrepancies. One coder independently coded the remainder of the interviews. PRINCIPAL FINDINGS: Several key themes were identified regarding facilitators and barriers for VA enrollment including reputation for high-quality VA care, convenience of VA services, awareness of VA services and benefits, and VA mental health services. Nearly every VA leader actively used tools and data to understand enrollment and retention rates and sought to enroll and retain more Veterans. To improve the management of enrollment and retention, VA leaders would like data shared in an easily understandable format and the capability to share data between the VA and community healthcare systems. CONCLUSIONS: Enrollment and retention information is important for healthcare leaders to guide their health system decisions. Various tools are currently being used to try to understand the data. However, a multifunctional tool is needed to better aggregate the data to provide VA leadership with key information on Veterans' enrollment and retention.

Limited Evidence of Shared Decision Making for Prostate Cancer Screening in Audio-Recorded Primary Care Visits Among Black Men and their Healthcare Providers.

Prostate-specific antigen (PSA)-based prostate cancer screening is a preference-sensitive decision for which experts recommend a shared decision making (SDM) approach. This study aimed to examine PSA screening SDM in primary care. Methods included qualitative analysis of audio-recorded patient-provider interactions supplemented by quantitative description. Participants included 5 clinic providers and 13 patients who were: (1) 40-69 years old, (2) Black, (3) male, and (4) attending clinic for routine primary care. Main measures were SDM element themes and "observing patient involvement in decision making" (OPTION) scoring. Some discussions addressed advantages, disadvantages, and/or scientific uncertainty of screening, however, few patients received all SDM elements. Nearly all providers recommended screening, however, only 3 patients were directly asked about screening preferences. Few patients were asked about prostate cancer knowledge (2), urological symptoms (3), or family history (6). Most providers discussed disadvantages (80%) and advantages (80%) of PSA screening. Average OPTION score was 25/100 (range 0-67) per provider. Our study found limited SDM during PSA screening consultations. The counseling that did take place utilized components of SDM but inconsistently and incompletely. We must improve SDM for PSA screening for diverse patient populations to promote health equity. This study highlights the need to improve SDM for PSA screening.

Lung Cancer Screening Before and After a Multifaceted Electronic Health Record Intervention: A Nonrandomized Controlled Trial.

Importance: Lung cancer is the deadliest cancer in the US. Early-stage lung cancer detection with lung cancer screening (LCS) through low-dose computed tomography (LDCT) improves outcomes. Objective: To assess the association of a multifaceted clinical decision support intervention with rates of identification and completion of recommended LCS-related services. Design, Setting, and Participants: This nonrandomized controlled trial used an interrupted time series design, including 3 study periods from August 24, 2019, to April 27, 2022: baseline (12 months), period 1 (11 months), and period 2 (9 months). Outcome changes were reported as shifts in the outcome level at the beginning of each period and changes in monthly trend (ie, slope). The study was conducted at primary care and pulmonary clinics at a health care system headquartered in Salt Lake City, Utah, among patients aged 55 to 80 years who had smoked 30 pack-years or more and were current smokers or had quit smoking in the past 15 years. Data were analyzed from September 2023 through February 2024. Interventions: Interventions in period 1 included clinician-facing preventive care reminders, an electronic health record-integrated shared decision-making tool, and narrative LCS guidance provided in the LDCT ordering screen. Interventions in period 2 included the same clinician-facing interventions and patient-facing reminders for LCS discussion and LCS. Main Outcome and Measure: The primary outcome was LCS care gap closure, defined as the identification and completion of recommended care services. LCS care gap closure could be achieved through LDCT completion, other chest CT completion, or LCS shared decision-making. Results: The study included 1865 patients (median [IQR] age, 64 [60-70] years; 759 female [40.7%]). The clinician-facing intervention (period 1) was not associated with changes in level but was associated with an increase in slope of 2.6 percentage points (95% CI, 2.4-2.7 percentage points) per month in care gap closure through any means and 1.6 percentage points (95% CI, 1.4-1.8 percentage points) per month in closure through LDCT. In period 2, introduction of patient-facing reminders was associated with an immediate increase in care gap closure (2.3 percentage points; 95% CI, 1.0-3.6 percentage points) and closure through LDCT (2.4 percentage points; 95% CI, 0.9-3.9 percentage points) but was not associated with an increase in slope. The overall care gap closure rate was 175 of 1104 patients (15.9%) at the end of the baseline period vs 588 of 1255 patients (46.9%) at the end of period 2. Conclusions and Relevance: In this study, a multifaceted intervention was associated with an improvement in LCS care gap closure. Trial Registration: ClinicalTrials.gov Identifier: NCT04498052.

Development of Complementary Encounter and Patient Decision Aids for Shared Decision Making about Stroke Prevention in Atrial Fibrillation.

Introduction: Decision aids (DAs) are helpful instruments used to support shared decision making (SDM). Patients with atrial fibrillation (AF) face complex decisions regarding stroke prevention strategies. While a few DAs have been made for AF stroke prevention, an encounter DA (EDA) and patient DA (PDA) have not been created to be used in conjunction with each other before. Design: Using iterative user-centered design, we developed 2 DAs for anticoagulation choice and stroke prevention in AF. Prototypes were created, and we elicited feedback from patients and experts via observations of encounters, usability testing, and semistructured interviews. Results: User testing was done with 33 experts (in AF and SDM) and 51 patients from 6 institutions. The EDA and PDA underwent 1 and 4 major iterations, respectively. Major differences between the DAs included AF pathophysiology and a preparation to meet with the clinician in the PDA as well as different language throughout. Content areas included personalized stroke risk, differences between anticoagulants, and risks of bleeding. Based on user feedback, developers 1) addressed feelings of isolation with AF, 2) improved navigation options, 3) modified content and flow for users new to AF and those experienced with AF, 4) updated stroke risk pictographs, and 5) added structure to the preparation for decision making in the PDA. Limitations: These DAs focus only on anticoagulation for stroke prevention and are online, which may limit participation for those less comfortable with technology. Conclusions: Designing complementary DAs for use in tandem or separately is a new method to support SDM between patients and clinicians. Extensive user testing is essential to creating high-quality tools that best meet the needs of those using them. Highlights: First-time complementary encounter and patient decision aids have been designed to work together or separately.User feedback led to greater structure and different experiences for patients naïve or experienced with anticoagulants in patient decision aids.Online tools allow for easier dissemination, use in telehealth visits, and updating as new evidence comes out.