Leading a Healthcare Organization During a Period of Social Change: A Qualitative Study of Chief Executive Officers.

BACKGROUND: In recent years, organizational leaders have faced growing pressure to respond to social and political issues. Although previous research has examined the experiences of corporate CEOs engaging in these issues, less is known about the perspectives of healthcare leaders. OBJECTIVE: To explore the experiences of healthcare CEOs engaging in health-related social and political issues, with a specific focus on systemic racism and abortion policy. DESIGN: Qualitative study using semi-structured interviews from February to July 2023. PARTICIPANTS: CEOs of US-based hospitals or health systems. APPROACH: One-on-one interviews which were audio recorded, professionally transcribed, and analyzed using thematic analysis. KEY RESULTS: This study included 25 CEOs of US-based hospitals or health systems. Almost half were between ages 60 and 69 (12 [48%]), 19 identified as male (76%), and 20 identified as White (80%). Approximately half self-identified as Democrats (13 [55%]). Most hospitals and health systems were private non-profits (15 [60%]). The interviews organized around four domains: (1) Perspectives on their Role, (2) Factors Impacting Engagement, (3) Improving Engagement, and (4) Experiences Responding to Recent Polarizing Events. Within these four domains, nine themes emerged. CEOs described increasing pressure to engage and had mixed feelings about their role. They identified personal, organizational, and political factors that affect their engagement. CEOs identified strategies to measure the success of their engagement and also reflected on their experiences speaking out about systemic racism and abortion legislation. CONCLUSIONS: In this qualitative study, healthcare CEOs described mixed perspectives on their role engaging in social and political issues and identified several factors impacting engagement. CEOs cited few strategies to measure the success of their engagement. Given that healthcare leaders are increasingly asked to address policy debates, more work is needed to examine the role and impact of healthcare CEOs engaging in health-related social and political issues.

Build or Buy? Managing the New Technology Decision Tree.

Technology plays a role in nearly every aspect of healthcare delivery. Health systems must continually invest in new and existing technology and analytics platforms to scale initiatives, enable innovation, and achieve interoperability to meet the needs and expectations of patients and clinicians while remaining focused on the organization's mission and strategic priorities. In this process, decision-makers must determine how to allocate technological resources to platforms that meet clinical and administrative needs while reducing the need for frequent replacement or reconfiguration. Advances in artificial intelligence and its capabilities add urgency and complexity to technology investment decisions. An important consideration during this process is when to build new technology infrastructure and when to partner with existing companies and buy technology solutions. This case study explores a major academic medical center's approach to that decision, including the factors that influenced it and the outcomes of two solutions that were developed in-house.

How Penn Medicine Is Advancing Care Delivery to Meet More Patients' Needs.

SUMMARY: The University of Pennsylvania Health System was founded in 1993 as one of the nation's first integrated academic medical centers. Over the past 29 years, Penn Medicine has systematically built a care delivery system based on three core values: innovation, integration, and impact. The operating strategy is designed to meet the patient's needs in a traditional brick-and-mortar hospital as well as in an increasingly virtual world. Today's patient is demanding an omnichannel experience with superior outcomes. Although long discussed in healthcare, such a comprehensive, seamless patient experience is only possible when all four channels of care delivery-hospital, ambulatory, home, and virtual-are sustainably integrated to improve the health of the population through digital innovation and analytics.The COVID-19 pandemic forced healthcare systems around the world to pour resources into telemedicine and other telehealth tools. This shift is fueling a dramatic shift from a piecemeal digital strategy to a comprehensive approach to the digital world where increased communication among clinicians, caregivers, and patients can lead to improved outcomes at a lower cost.In this article, we present an illustrative case study focusing on two of four channels of care: the Pavilion at the Hospital of the University of Pennsylvania, where the latest in digital innovation has been built into the walls of our most ambitious capital project to date, and Penn Medicine at Home, which provides home care services, home infusion, and hospice care to patients throughout the region. The new $1.6 billion Pavilion and its technological updates have been seamlessly woven into the longstanding Penn Medicine at Home program. As a system, we did this by learning from both the victories and the setbacks to design for a future of healthcare we could once only imagine.

Difference Between Users and Nonusers of a Patient Portal in Health Behaviors and Outcomes: Retrospective Cohort Study.

BACKGROUND: Patient portals are frequently used in modern health care systems as an engagement and communication tool. An increased focus on the potential value of these communication channels to improve health outcomes is warranted. OBJECTIVE: This paper aimed to quantify the impact of portal use on patients' preventive health behavior and chronic health outcomes. METHODS: We conducted a retrospective, observational cohort study of 10,000 patients aged 50 years or older who were treated at the University of Pennsylvania Health System (UPHS) from September 1, 2014, to October 31, 2016. The data were sourced from the UPHS electronic health records. We investigated the association between patient portal use and patients' preventive health behaviors or chronic health outcomes, controlling for confounders using a novel cardinality matching approach based on propensity scoring and a subsequent bootstrapping method to estimate the variance of association estimates. RESULTS: Patient-level characteristics differed substantially between portal users, comprising approximately 59.32% (5932/10000) of the cohort, and nonusers. On average, users were more likely to be younger (63.46 years for users vs 66.08 years for nonusers), white (72.77% [4317/5932] for users vs 52.58% [2139/4068] for nonusers), have commercial insurance (60.99% [3618/5932] for users vs 40.12% [1632/4068] for nonusers), and have higher annual incomes (US $74,172/year for users vs US $62,940/year for nonusers). Even after adjusting for these potential confounders, patient portal use had a positive and clinically meaningful impact on patients' preventive health behaviors but not on chronic health outcomes. CONCLUSIONS: This paper contributes to the understanding of the impact of patient portal use on health outcomes and is the first study to identify a meaningful subgroup of patients' health behaviors that improved with portal use. These findings may encourage providers to promote portal use to improve patients' preventive health behaviors.

Determinants of hospital outcomes for patients with COVID-19 in the University of Pennsylvania Health System.

There is growing evidence that racial and ethnic minorities bear a disproportionate burden from COVID-19. Temporal changes in the pandemic epidemiology and diversity in the clinical course require careful study to identify determinants of poor outcomes. We analyzed 6255 hospitalized individuals with PCR-confirmed SARS-CoV-2 infection from one of 5 hospitals in the University of Pennsylvania Health System between March 2020 and March 2021, using electronic health records to assess risk factors and outcomes through 8 weeks post-admission. Discharge, readmission and mortality outcomes were analyzed in a multi-state model with multivariable Cox models for each transition. Mortality varied markedly over time, with cumulative incidence (95% CI) 30 days post-admission of 19.1% (16.9, 21.3) in March-April 2020, 5.7% (4.2, 7.5) in July-October 2020 and 10.5% (9.1,12.0) in January-March 2021; 26% of deaths occurred after discharge. Average age (SD) at admission varied from 62.7 (17.6) to 54.8 (19.9) to 60.5 (18.1); mechanical ventilation use declined from 21.3% to 9-11%. Compared to Caucasian, Black race was associated with more severe disease at admission, higher rates of co-morbidities and residing in a low-income zip code. Between-race risk differences in mortality risk diminished in multivariable models; while admitting hospital, increasing age, admission early in the pandemic, and severe disease and low blood pressure at admission were associated with increased mortality hazard. Hispanic ethnicity was associated with fewer baseline co-morbidities and lower mortality hazard (0.57, 95% CI: 0.37, .087). Multi-state modeling allows for a unified framework to analyze multiple outcomes throughout the disease course. Morbidity and mortality for hospitalized COVID-19 patients varied over time but post-discharge mortality remained non-trivial. Black race was associated with more risk factors for morbidity and with treatment at hospitals with lower mortality. Multivariable models suggest there are not between-race differences in outcomes. Future work is needed to better understand the identified between-hospital differences in mortality.

Learning from local to global: An efficient distributed algorithm for modeling time-to-event data.

OBJECTIVE: We developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites. MATERIALS AND METHODS: Using patient-level data from a single site combined with only aggregated information from other sites, we constructed a surrogate likelihood function, approximating the Cox partial likelihood function obtained using patient-level data from all sites. By maximizing the surrogate likelihood function, each site obtained a local estimate of the model parameter, and the ODAC estimator was constructed as a weighted average of all the local estimates. We evaluated the performance of ODAC with (1) a simulation study and (2) a real-world use case study using 4 datasets from the Observational Health Data Sciences and Informatics network. RESULTS: On the one hand, our simulation study showed that ODAC provided estimates nearly the same as the estimator obtained by analyzing, in a single dataset, the combined patient-level data from all sites (ie, the pooled estimator). The relative bias was <0.1% across all scenarios. The accuracy of ODAC remained high across different sample sizes and event rates. On the other hand, the meta-analysis estimator, which was obtained by the inverse variance weighted average of the site-specific estimates, had substantial bias when the event rate is <5%, with the relative bias reaching 20% when the event rate is 1%. In the Observational Health Data Sciences and Informatics network application, the ODAC estimates have a relative bias <5% for 15 out of 16 log hazard ratios, whereas the meta-analysis estimates had substantially higher bias than ODAC. CONCLUSIONS: ODAC is a privacy-preserving and noniterative method for implementing time-to-event analyses across multiple sites. It provides estimates on par with the pooled estimator and substantially outperforms the meta-analysis estimator when the event is uncommon, making it extremely suitable for studying rare events and diseases in a distributed manner.

Effect of an Automated Patient Dashboard Using Active Choice and Peer Comparison Performance Feedback to Physicians on Statin Prescribing: The PRESCRIBE Cluster Randomized Clinical Trial.

Importance: Statins are not prescribed to approximately 50% of patients who could benefit from them. Objective: To evaluate the effectiveness of an automated patient dashboard using active choice framing with and without peer comparison feedback on performance to nudge primary care physicians (PCPs) to increase guideline-concordant statin prescribing. Design, Setting, and Participants: This 3-arm cluster randomized clinical trial was conducted from February 21, 2017, to April 21, 2017, at 32 practice sites in Pennsylvania and New Jersey. Participants included 96 PCPs and 4774 patients not previously receiving statin therapy. Data were analyzed from April 25, 2017, to June 16, 2017. Interventions: Primary care physicians in the 2 intervention arms were emailed a link to an automated online dashboard listing their patients who met national guidelines for statin therapy but had not been prescribed this medication. The dashboard included relevant patient information, and for each patient, PCPs were asked to make an active choice to prescribe atorvastatin, 20 mg, once daily, atorvastatin at another dose, or another statin or not prescribe a statin and select a reason. The dashboard was available for 2 months. In 1 intervention arm, the email to PCPs also included feedback on their statin prescribing rate compared with their peers. Primary care physicians in the usual care group received no interventions. Main Outcomes and Measures: Statin prescription rates. Results: Patients had a mean (SD) age of 62.4 (8.3) years and a mean (SD) 10-year atherosclerotic cardiovascular disease risk score of 13.6 (8.2); 2625 (55.0%) were male, 3040 (63.7%) were white, and 1318 (27.6%) were black. In the active choice arm, 16 of 32 PCPs (50.0%) accessed the patient dashboard, but only 2 of 32 (6.3%) signed statin prescription orders. In the active choice with peer comparison arm, 12 of 32 PCPs (37.5%) accessed the patient dashboard and 8 of 32 (25.0%) signed statin prescription orders. Statins were prescribed in 40 of 1566 patients (2.6%) in the usual care arm, 116 of 1743 (6.7%) in the active choice arm, and 117 of 1465 (8.0%) in the active choice with peer comparison arm. In the main adjusted model, compared with usual care, there was a significant increase in statin prescribing in the active choice with peer comparison arm (adjusted difference in percentage points, 5.8; 95% CI, 0.9-13.5; P = .008), but not in the active choice arm (adjusted difference in percentage points, 4.1; 95% CI, -0.8 to 13.1; P = .11). Conclusions and Relevance: An automated patient dashboard using both active choice framing and peer comparison feedback led to a modest but significant increase in guideline-concordant statin prescribing rates. Trial Registration: ClinicalTrials.gov Identifier: NCT03021759.