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Background The aim of this study was to identify the range of ways that safety net hospitals (SNHs) have been empirically operationalized in the literature and determine the extent to which patterns could be identified in the use of empirical definitions of SNHs. Methods We conducted a PRISMA guided systematic review of studies published between 2009 and 2018 and analyzed 22 articles that met the inclusion criteria of hospital-level analyses with a clear SNH definition. Results Eleven unique SNH definitions were identified, and there were no obvious patterns in the use of a definition category (Medicaid caseload, DSH payment status, uncompensated care, facility characteristics, patient care mix) by the journal type where the article appeared, dataset used, or the year of publication. Conclusions Overall, there is broad variability in the conceptualization of, and variables used to define, SNHs. Our work advances the field toward the development of standards in measuring, operationalizing, and conceptualizing SNHs across research and policy questions.

Background Patient engagement is seen as a necessary component in achieving the triple aim of improved population health, improved experience of care, and lower per capita health care costs. While there has been a substantial increase in the number of tools and patient-centered initiatives designed to help patients participate in health decisions, there remains a limited understanding of engagement from the perspective of patients and a lack of measures designed to capture the multi-faceted nature of the concept. Methods Development of a concept map of patient engagement followed a five-step modified Group Concept Mapping (GCM) methodology of preparation, generation, structuring, analysis and interpretation. We engaged a Project Advisory Committee at each step, along with three rounds of survey collection from clinicians and patients for element generation (272 clinicians, 61 patients), statement sorting (30 clinicians, 15 patients), and ranking and rating of statements (159 clinicians, 67 patients). The survey of three separate samples, as opposed to focus groups of ‘experts,’ was an intentional decision to gain a broad perspective about the concept of patient engagement. We conducted the structure and analysis steps within the groupwisdom concept mapping software. Results The final concept map comprised 47 elements organized into 5 clusters: Relationship with Provider, Patient Attitudes and Behaviors, Access, Internal Resources and External Resources. There was considerable agreement in the way elements in each cluster were rated by patients and clinicians. An analysis of the importance of the constitutive elements of patient engagement relative to their addressability highlighted actionable items in the domain of Relationship with Provider, aimed at building trust and enabling patients to ask questions. At the same time, the analysis also identified elements traditionally considered barriers to engagement, like personal access to the internet and the patient’s level of digital literacy, as difficult to address by the healthcare system, but also relatively less important for patients. Conclusions Through our GCM approach, incorporating perspectives of both patients and clinicians, we identified items that can be used to assess patient engagement efforts by healthcare systems. As a result, our study offers specific insight into areas that can be targeted for intervention by healthcare systems to improve patient engagement.

ObjectivePatient engagement (PE), or a patient’s participation in their healthcare, is an important component of comprehensive healthcare delivery, yet there is not an existing, publicly available, measurement tool to assess PE capacity and behaviours. We sought to develop a survey to measure PE capacity and behaviours for use in ambulatory healthcare clinics.DesignMeasure development and psychometric evaluation.Setting and participantsA total of 1180 adults in the USA from 2022 to 2024, including 1050 individuals who had indicated they had seen a healthcare provider in the prior 12 months who were recruited nationally via social media across three separate samples; 8 patient advisors and healthcare providers recruited from a large, midwestern US Academic Medical Center; and 122 patients recruited from five participating ambulatory clinics in the Midwestern USA.MethodsAn initial survey was developed based on a concept mapping approach with a Project Advisory Board composed of patients, researchers and clinicians. Social media was then used to recruit 540 participants nationally (Sample 1) to complete the initial, 101-item version of the survey to generate data for factor analysis. We conducted exploratory and confirmatory factor analyses to assess model and item fit to inform item reduction, and subsequently conducted cognitive interviews with eight additional participants (patient advisors and providers; Sample 2), who read survey items aloud, shared their thoughts and selected a response. The survey was revised and shortened based on these results. Next, a test–retest survey, also administered nationally via another round of social media recruitment, was administered two times to a separate sample (n=155; Sample 3), 2 weeks apart. We further revised the survey to remove items with low temporal stability based on these results. For clinic administration, research staff approached patients (n=122; Sample 4) in waiting rooms in one of five ambulatory clinics to complete the survey electronically or on paper to determine feasibility of in-clinic survey completion. We engaged in further item reduction based on provider feedback about survey length and fielded a final revised and shortened survey nationally via a final round of social media recruitment (n=355; Sample 5) to obtain psychometric data on this final version.Primary and secondary outcome measuresCronbach’s alphas, intraclass correlations (ICCs), Comparative Fit Index (CFI), root mean square error of approximation (RMSEA), standardised root mean squared residual (SRMR).ResultsThe final PE Capacity Survey (PECS) includes six domains across two scales: ‘engagement behaviours’ (ie, preparing for appointments, ensuring understanding, adhering to care) and ‘engagement capacity’ (ie, healthcare navigation resources, resilience, relationship with provider). The PECS is 18 questions, can be completed during a clinic visit in less than 10 minutes, and produces scores which demonstrate acceptable internal consistency reliability (α=0.72 engagement behaviours, 0.76 engagement capacity), indicating items are measuring the same overarching construct. The scales also had high test–retest reliability (ICC=0.82 behaviours, 0.86 capacity), indicating stability of response over time, and expected dimensionality with high fit indices for the final scales (behaviours: CFI=0.97; RMSEA=0.07; SRMR=0.05; capacity: CFI=0.99; RMSEA=0.06; SRMR=0.06), indicating initial evidence of construct validity.ConclusionsThe PECS is the first known measure to assess patients’ capacity for engagement and represents a step toward informing interventions and care plans that acknowledge a patient’s engagement capacity and supporting engagement behaviours. Future work should be done to validate the measure in other languages and patient populations, and to assess criterion-related validity of the measure against patient outcomes.

Antiplatelets and statins are efficacious for preventing future cardiovascular events in patients with coronary heart disease. Disparity in cardiovascular outcomes exists by race/ethnicity and gender; however, few studies have explored potential disparities in long-term antiplatelet and statin use by race/ethnicity and gender. We conducted a repeated cross-sectional analysis using the nationally representative Medical Expenditure Panel Survey from 2003 to 2012. The sample consisted of 14,334 men and women >29 years with coronary heart disease. We identified antiplatelet and statin use, medical conditions, and sociodemographic characteristics. Rates of use did not change for statins or the combination of statins and antiplatelets from 2003 to 2012 but decreased for antiplatelets (p = 0.015). Of the total sample, 70.9% (95% confidence interval [CI] 69.7 to 72.1) reported use of antiplatelets, 52.5% (95% CI 51.1 to 53.8) reported statin use, and 43.1% (95% CI 41.8 to 44.4) reported the combination. Use of antiplatelets and statins were associated with one another (odds ratio 3.22; 95% CI 2.87 to 3.62). From 2009 to 2012, black and Hispanic men along with all race/ethnicities of women were less likely to report use of statins, antiplatelets, and the combination of the 2 compared with white men, even after controlling for sociodemographics. Changing the definition of a medication use, inclusion of cardiovascular risk factors, or the inclusion of warfarin in the antiplatelet category did not substantially change the results. Future practice and policy goals should focus on increasing the number of high-risk patients on appropriate preventative medications while focusing particular attention on decreasing the identified disparity.

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the United States. Early detection via routine CRC screening can significantly lower risks for CRC-specific morbidity and mortality. Public health initiatives between 2000 and 2015 nearly doubled CRC screening rates for some US adults. However, screening rates remain lowest for adults aged 45-49 years (20%), patients of safety net health care facilities (42%), adults without insurance (44%), and other subgroups compared with national averages (72%). Given the evolving landscape of digital health care and trends in web-based health information-seeking behaviors, leveraging online medical record (OMR) systems may be an underutilized resource to promote CRC screening utilization. Recognizing trends in OMR usage and patient demographics may enhance digital inclusion-a key social determinant of health-and support equitable web-based interventions aimed at boosting CRC screening across diverse populations. This study examined the association of accessing an OMR with CRC screening utilization and corresponding sociodemographic characteristics of US adults. In 2023, we conducted a secondary data analysis using a pooled, weighted sample from Health Information National Trends Survey (HINTS) 5 cycles, 2, 3, and 4 (2018-2020), a nationally representative survey assessing how US adults access and use health-related information. We analyzed the association between sociodemographic characteristics, medical conditions, OMR access, and CRC screening behaviors via logistic regression. The sample included adults aged 45-75 years (N=5143). The mean age was 59 (SD 8) years for those who reported CRC screening and 52 (SD 6) years for those never screened. Nearly 70% (4029/5143) of participants reported CRC screening and 52% (2707/5143) reported OMR access in the past year. Adjusted odds of CRC screening were higher among non-Hispanic African American or Black adults than among non-Hispanic White adults (odds ratio [OR] 1.76, 95% CI 1.22-2.53), adults who accessed an OMR (OR 1.89, 95% CI 1.45-2.46), older individuals (OR 1.18, 95% CI 1.16-1.21), the insured (OR 3.69, 95% CI 2.34-5.82), and those with a professional or graduate degree versus those with a high school diploma or less (OR 2.65, 95% CI 1.28-5.47). Individuals aged 65-75 years were significantly more likely (P<.001) to be screened (1687/1831, 91%) than those aged 45-49 years (190/610, 29%). Promoting OMR access, especially among the most disadvantaged Americans, may assist in reaching national screening goals. Emphasis should be placed on the mutability of OMR use compared with most other statistically significant associations with CRC screening behaviors. OMR access provides an intervenable means of promoting CRC education and screening, especially among those facing structural barriers to cancer diagnoses and care. Future research should focus on tailored and accessible interventions that expand OMR access, particularly for younger populations.

Many of the benefits of electronic health records (EHRs) have not been achieved at expected levels because of a variety of unintended negative consequences such as documentation burden. Previous studies have characterized EHR use during and outside work hours, with many reporting that physicians spend considerable time on documentation-related tasks. These studies characterized EHR use during and outside work hours using clock time versus actual physician clinic schedules to define the outside work time. This study aimed to characterize EHR work outside scheduled clinic hours among primary care pediatricians using a retrospective descriptive task analysis of EHR access log data and actual physician clinic schedules to define work time. We conducted a retrospective, exploratory, descriptive task analysis of EHR access log data from primary care pediatricians in September 2019 at a large Midwestern pediatric health center to quantify and identify actions completed outside scheduled clinic hours. Mixed-effects statistical modeling was used to investigate the effects of age, sex, clinical full-time equivalent status, and EHR work during scheduled clinic hours on the use of EHRs outside scheduled clinic hours. Primary care pediatricians (n=56) in this study generated 1,523,872 access log data points (across 1069 physician workdays) and spent an average of 4.4 (SD 2.0) hours and 0.8 (SD 0.8) hours per physician per workday engaged in EHRs during and outside scheduled clinic hours, respectively. Approximately three-quarters of the time working in EHR during or outside scheduled clinic hours was spent reviewing data and reports. Mixed-effects regression revealed no associations of age, sex, or clinical full-time equivalent status with EHR use during or outside scheduled clinic hours. For every hour primary care pediatricians spent engaged with the EHR during scheduled clinic hours, they spent approximately 10 minutes interacting with the EHR outside scheduled clinic hours. Most of their time (during and outside scheduled clinic hours) was spent reviewing data, records, and other information in EHR.

Background Healthcare-associated infections (HAIs) impact patients’ lives through prolonged hospitalization, morbidity, and death, resulting in significant costs to both health systems and society. Central line-associated bloodstream infections (CLABSIs) and catheter-associated urinary tract infections (CAUTIs) are two of the most preventable HAIs. As a result, these HAIs have been the focus of significant efforts to identify evidence-based clinical strategies to reduce infection rates. The Comprehensive Unit-based Safety Program (CUSP) provides a formal model for translating CLABSI-reduction evidence into practice. Yet, a national demonstration project found organizations experienced variable levels of success using CUSP to reduce CLABSIs. In addition, in Fiscal year 2019, Medicare will expand use of CLABSI and CAUTI metrics beyond ICUs to the entire hospital for reimbursement purposes. As a result, hospitals need guidance about how to successfully translate HAI-reduction efforts such as CUSP to non-ICU settings (clinical practice), and how to shape context (management practice)—including culture and management strategies—to proactively support clinical teams. Methods Using a mixed-methods approach to evaluate the contribution of management factors to successful HAI-reduction efforts, our study aims to: (1) Develop valid and reliable measures of structural management practices associated with the recommended CLABSI Management Strategies for use as a survey (HAI Management Practice Guideline Survey) to support HAI-reduction efforts in both medical/surgical units and ICUs; (2) Develop, validate, and then deploy the HAI Management Practice Guideline Survey, first across Ohio hospitals, then nationwide, to determine the positive predictive value of the measurement instrument as it relates to CLABSI- and CAUTI-prevention; and (3) Integrate findings into a Management Practices Toolkit for HAI reduction that includes an organization-specific data dashboard for monitoring progress and an implementation program for toolkit use, and disseminate that Toolkit nationwide. Discussion Providing hospitals with the tools they need to successfully measure management structures that support clinical care provides a powerful approach that can be leveraged to reduce the incidence of HAIs experienced by patients. This study is critical to providing the information necessary to successfully “make health care safer” by providing guidance on how contextual factors within a healthcare setting can improve patient safety across hospitals.

Children dependent on mechanical ventilation are a vulnerable population by virtue of their chronic disability and are therefore at increased risk for health disparities and access barriers. The present study is the first, to our knowledge, to conduct a large-scale survey of caregivers of ventilator-dependent children to develop a comprehensive socio-demographic profile. To describe the demographic and health status profile of ventilator-dependent children, to identify the types of unmet needs families caring for a child on a ventilator face, and to determine the correlates of access to care coordination. A survey was administered to 122 parents whose children attended a pediatric home ventilator clinic at a large tertiary Midwestern medical center (84% of the clinic population). Half of the patient population had severe functional limitations, and 70% had one or more comorbidities. One quarter of caregivers reported current financial struggles, and 16% screened positive for a probable depressive disorder. More than half of families reported unmet needs for care, most frequently therapeutic services and skilled nursing care. Of those reporting an unmet need for skilled nursing care, lack of adequate staffing was the main barrier (71.1%). Financial struggles and a probable caregiver depressive disorder were significantly associated with an unmet need for care coordination. This is the first large-scale quantitative study to investigate the themes of unmet need and care coordination within this vulnerable population. The results suggest these families face barriers accessing therapeutic and skilled nursing services, and caregiver mental health and financial struggles may be important points of intervention for service providers through the inclusion of multidisciplinary care teams and the strengthening of social services referral networks.

The mission of healthcare organizations has undergone a remarkable transformation from curing disease to caring for the well-being of populations. While health care policy has explored this domain before, notably in Health Maintenance Organizations, the current efforts to create Patient-Centered Medical Homes (PCMHs) and Accountable Care Organizations (ACOs) represent a broader effort to move organizational responsibilities towards population health and care management. Volume 16 of AHCM presents papers that explore this topic across various levels of the healthcare system and employing multiple research designs including case studies, theoretical pieces, secondary data analyses, survey research and qualitative methodologies. Aspects of health care organization discussed in this volume include the PCMH, ACOs, integration with the public health and mental health systems, hospital-physician alignment, and resource planning. Population health management is presented as a factor driving these organizational changes and as a mechanism to facilitate this change.

To characterize factors influencing the development and sustainability of data sharing in the Mid-Ohio Farmacy (MOF), a produce referral program implemented in partnership between a community-based organization (the Mid-Ohio Food Collective [\"Food Collective\"]) and an academic medical center (The Ohio State University Wexner Medical Center [OSUWMC]). We used an in-depth case study approach to identify challenges that arose during implementation of the MOF and related solutions via semistructured interviews with representatives of both organizations (May-September 2020). Key informants from OSUWMC (n = 20) and the Food Collective (n = 11) were identified using a combination of purposive and convenience sampling; they included administrators, project champions, clinical providers, and food pantry representatives. Interview transcripts were coded using a deductive dominant approach guided by a logic model aimed at determining the resources and activities relevant to the development of the partnership. Challenges of cross-sector data sharing fit into 3 themes: data sharing regulations, data exchange capabilities, and cross-sector data integration. Overcoming these challenges required creative workarounds-for example, linking patients across organizations was done via establishment of a unique, partnership-specific patient identifier, which was incorporated into the health system's electronic health record for continuity. Our findings suggest that current regulatory frameworks are misspecified to the growing interest in cross-sector partnerships between health care and community-based organizations. Future efforts to support these relationships should consider clarifying rules around data sharing and increasing Medicaid support for nonmedical, health-related social needs.