Association of women leaders in the C-suite with hospital performance

BackgroundWomen comprise 50% of the healthcare workforce, but only about 25% of senior leadership positions in the USA. No studies to our knowledge have investigated the performance of hospitals led by women versus those led by men to evaluate the potential explanation that the inequity reflects appropriate selection due to skill or performance differences.MethodsWe conducted a descriptive analysis of the gender composition of hospital senior leadership (C-suite) teams and cross-sectional, regression-based analyses of the relationship between gender composition, hospital characteristics (eg, location, size, ownership), and financial, clinical, safety, patient experience and innovation performance metrics using 2018 data for US adult medical/surgical hospitals with >200 beds. C-suite positions examined included chief executive officer (CEO), chief financial officer (CFO) and chief operating officer (COO). Gender was obtained from hospital web pages and LinkedIn. Hospital characteristics and performance were obtained from American Hospital Directory, American Hospital Association Annual Hospital Survey, Healthcare Cost Report Information System and Hospital Consumer Assessment of Healthcare Providers and Systems surveys.ResultsOf the 526 hospitals studied, 22% had a woman CEO, 26% a woman CFO and 36% a woman COO. While 55% had at least one woman in the C-suite, only 15.6% had more than one. Of the 1362 individuals who held one of the three C-suite positions, 378 were women (27%). Hospital performance on 27 of 28 measures (p>0.05) was similar between women and men-led hospitals. Hospitals with a woman CEO performed significantly better than men-led hospitals on one financial metric, days in accounts receivable (p=0.04).ConclusionHospitals with women in the C-suite have comparable performance to those without, yet inequity in the gender distribution of leaders remains. Barriers to women’s advancement should be recognised and efforts made to rectify this inequity, rather than underusing an equally skilled pool of potential women leaders.

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The effect of guidelines, policy initiatives, and feedback tools on hospital adherence with the 12-node measure for colon cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.31_suppl.65 ◽

2013 ◽

Vol 31 (31_suppl) ◽

pp. 65-65

Author(s):

Jennifer L. Paruch ◽

Ryan P. Merkow ◽

Mila H. Ju ◽

David Porter Winchester ◽

Clifford Y. Ko ◽

...

Keyword(s):

Colon Cancer ◽

Hospital Performance ◽

National Cancer Data Base ◽

Process Measures ◽

American Hospital ◽

American Hospital Association ◽

Cancer Data ◽

Policy Initiatives ◽

Academic Hospitals ◽

Hospital Characteristics

65 Background: Examination of > = 12 lymph nodes after colectomy is one of few surgical process measures. Several initiatives have targeted this measure; one tool developed by the Commission on Cancer (CoC) provides benchmarked feedback on hospital performance (CP3R). Our objectives were to (1) examine changes in measure performance over time in response to guidelines, policy initiatives, and feedback, and (2) identify hospital characteristics associated with failure to improve adherence. Methods: Patients having surgery for Stage I-III colon cancer (1990-2010) were identified from the National Cancer Data Base (NCDB). For hospital-level analyses, NCDB and American Hospital Association (2010) data were merged. Hospital CP3R use was obtained from the user log system. Multivariable logistic regression adjusted for age and tumor factors was used to identify hospital characteristics associated with adherence in 2009-2010 (> = 12 nodes in > 80% of patients). Results: The percentage of patients with > = 12 nodes removed increased from 31.5% in 1990 to 84.1% in 2010 (p < 0.0001). The percentage of adherent hospitals increased from 2.2% in 1990 to 70% in 2010 (p < 0.0001). The steepest increase in adherence occurred with introduction of CP3R. Median CP3R use increased from 5 to 57 logins annually (2005-2010). Hospital predictors of poor adherence included low volume, community hospital type, private ownership, rural location, and lower number of specialists (Table). Conclusions: Guidelines, policy initiatives, and feedback tools have helped dramatically increase adherence with the 12-node measure, but small, non-academic hospitals have been slower to improve. Additional efforts are needed to understand barriers and improve adherence at these facilities. [Table: see text]

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Electronic health record adoption in US hospitals: the emergence of a digital “advanced use” divide

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocx080 ◽

2017 ◽

Vol 24 (6) ◽

pp. 1142-1148 ◽

Cited By ~ 87

Author(s):

Julia Adler-Milstein ◽

A Jay Holmgren ◽

Peter Kralovec ◽

Chantal Worzala ◽

Talisha Searcy ◽

...

Keyword(s):

Information Technology ◽

Performance Measurement ◽

Patient Engagement ◽

Safety Net ◽

Hospital Performance ◽

Critical Access Hospitals ◽

American Hospital ◽

American Hospital Association ◽

Ehr Adoption ◽

Electronic Health

Abstract Objective While most hospitals have adopted electronic health records (EHRs), we know little about whether hospitals use EHRs in advanced ways that are critical to improving outcomes, and whether hospitals with fewer resources – small, rural, safety-net – are keeping up. Materials and Methods Using 2008–2015 American Hospital Association Information Technology Supplement survey data, we measured “basic” and “comprehensive” EHR adoption among hospitals to provide the latest national numbers. We then used new supplement questions to assess advanced use of EHRs and EHR data for performance measurement and patient engagement functions. To assess a digital “advanced use” divide, we ran logistic regression models to identify hospital characteristics associated with high adoption in each advanced use domain. Results We found that 80.5% of hospitals adopted at least a basic EHR system, a 5.3 percentage point increase from 2014. Only 37.5% of hospitals adopted at least 8 (of 10) EHR data for performance measurement functions, and 41.7% of hospitals adopted at least 8 (of 10) patient engagement functions. Critical access hospitals were less likely to have adopted at least 8 performance measurement functions (odds ratio [OR] = 0.58; P < .001) and at least 8 patient engagement functions (OR = 0.68; P = 0.02). Discussion While the Health Information Technology for Economic and Clinical Health Act resulted in widespread hospital EHR adoption, use of advanced EHR functions lags and a digital divide appears to be emerging, with critical-access hospitals in particular lagging behind. This is concerning, because EHR-enabled performance measurement and patient engagement are key contributors to improving hospital performance. Conclusion Hospital EHR adoption is widespread and many hospitals are using EHRs to support performance measurement and patient engagement. However, this is not happening across all hospitals.

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Association of partial nephrectomy and presence of robotic surgery for kidney cancer in the United States.

Journal of Clinical Oncology ◽

10.1200/jco.2014.32.4_suppl.484 ◽

2014 ◽

Vol 32 (4_suppl) ◽

pp. 484-484

Author(s):

Steven V. Kardos ◽

Brian Shuch ◽

Peter Schulam ◽

Quoc-Dien Trinh ◽

Maxine Sun ◽

...

Keyword(s):

Robotic Surgery ◽

Partial Nephrectomy ◽

Multivariable Analysis ◽

The United States ◽

Population Based ◽

Cancer Center ◽

Treatment Technology ◽

American Hospital ◽

American Hospital Association ◽

Hospital Characteristics

484 Background: While hospital and surgeon characteristics are associated with the type of nephrectomy performed for renal cell carcinoma (RCC), it is unknown whether hospital presence of robotic surgery increases the likelihood of patients receiving partial nephrectomy (PN). Therefore, we evaluate the relationship of PN and hospital presence of robotic surgery from a population-based cohort in the U.S. Methods: After merging the Nationwide Inpatient Sample (NIS) and the American Hospital Association (AHA) survey from 2006 to 2008, we identified 21,999 patients who underwent either PN or radical nephrectomy (RN) for RCC. The primary outcome of this study was the type of nephrectomy performed. Multivariable logistic regression was used to identify hospital characteristics associated with receipt of PN, after adjusting for patient case mix. Results: Overall, we identified 4,832 (22.0%) and 16,347 (88.0%) patients who were surgically treated for RCC with PN and RN, respectively. On multivariable analysis, patients undergoing surgery were more likely to receive PN at academic (OR: 2.77;p<0.001), urban (OR: 3.66; p<0.001), and American College of Surgeon (ACOS) designated cancer centers (OR: 1.10; p<0.05) compared to non-academic, rural, and non-designated hospitals, respectively. After adjusting for patient and hospital characteristics, patients undergoing surgery at hospitals with presence of robotic surgery were also associated with higher adjusted odds ratios for receipt of PN compared to those treated at hospitals without the presence of this advanced treatment technology (OR: 1.28; p<0.001). Conclusions: While academic status and urban locations are established characteristics influencing the type of nephrectomy performed for RCC, ACOS cancer center designation and hospital presence of robotic surgery were also associated with higher use of PN. Our results are informative in identifying key hospital characteristics which may facilitate greater adoption of PN.

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DISSEMINATION AND CHARACTERISTICS OF ACUTE CARE FOR ELDERS (ACE) UNITS IN THE UNITED STATES

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462303000205 ◽

2003 ◽

Vol 19 (1) ◽

pp. 220-227 ◽

Cited By ~ 22

Author(s):

Ravishankar Jayadevappa ◽

Bernard S. Bloom ◽

Donna Brady Raziano ◽

Risa Lavizzo-Mourey

Keyword(s):

Logistic Regression ◽

Acute Care ◽

Average Length ◽

The Elderly ◽

The United States ◽

Economic Benefits ◽

Stepwise Logistic Regression ◽

American Hospital ◽

American Hospital Association ◽

Hospital Characteristics

Objective: The objective of this paper is to determine prevalence and characteristics of acute care for elders (ACE) units and hospital characteristics associated with the presence of an ACE unit.Methods: Data on characteristics and prevalence of ACE units were obtained by surveying all established geriatric medical divisions across U.S. medical schools. Data on hospital characteristics such as number of beds, revenue, number of Medicare inpatients, and average length of stay were obtained from the 1999 American Hospital Association Annual Survey Data. Descriptive statistics and t test were used to analyze the characteristics of ACE units. Stepwise logistic regression was used to analyze the hospital characteristics associated with the presence of an ACE unit.Results: The survey identified 16 geriatric divisions and programs with ACE units. Hospitals that have ACE units differ significantly with respect to number of beds and total revenue, compared with institutions that do not have an ACE unit. Stepwise logistic regression indicated total hospital revenue was the only factor significantly associated with the presence of an ACE unit.Conclusions: ACE units are attractive interdisciplinary models to address the particular needs of the elderly during their hospital stay. Low presence of ACE units warrants further research as to reasons more hospitals have not included them, given the available evidence for clinical, functional, and economic benefits.

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Abstract W P311: Demographic and Regional Characteristics Associated With Advanced Primary Stroke Center Designation

Stroke ◽

10.1161/str.45.suppl_1.wp311 ◽

2014 ◽

Vol 45 (suppl_1) ◽

Author(s):

Catherine McDonald ◽

Steven Cen ◽

Lucas Ramirez ◽

William J Mack ◽

Nerses Sanossian

Keyword(s):

Univariate Analysis ◽

American Hospital ◽

American Hospital Association ◽

Healthcare Facilities ◽

For Profit ◽

Home Value ◽

Regional Factors ◽

Stroke Center ◽

Zip Code ◽

Hospital Characteristics

Background: Nationwide less than a third of eligible hospitals have achieved advanced certification in stroke, and there may be disparities affecting less affluent areas serving a higher proportion of minorities. We aimed to characterize demographic and regional factors associated with achievement of stroke center certification while controlling for hospital characteristics. Methods: We linked the 2011 American Hospital Association survey of hospital characteristics to the 2010 national census for population and household data by region. Emergency medical services stroke routing data was obtained from communication with state and county contact. Only hospitals with ≥ 25 beds and 24-hour emergency departments were evaluated. The Joint Commission, Healthcare Facilities Accreditation Program and DNV Healthcare websites were used to determine certification status of each hospital. We controlled for hospital bed size, teaching affiliation (AMA, ACGME), emergency department volume, rural designation, hospital type (governmental/for-profit/nonprofit), and trauma center designation in analysis. Results: Of the 3696 hospitals to complete the survey, the 3069 fulfilling study criteria included 908 PSC (31%) and 2161 non-PSC. In univariate analysis PSC hospitals were located in areas with greater population in immediate vicinity (29, 316 vs. 20,901, p<0.0001), greater proportion of minorities (73% white, 16% black, 15% Hispanic vs. 80%/12%/11%, p<0.0001), greater number of households per zip code (11,540 vs. 8050, p<0.0001) and a higher regional mean income ($52,112 vs. $46,262, p<0.0001) and higher home value ($234,000 vs. $170,000, p<0.0001). More PSC hospitals were located in regions with preferential EMS routing of stroke (52% vs. 40%). While controlling for hospital-based factors, the demographic and regional factors independently associated with hospital PSC designation were number of households per zip code (per 1000 households OR 1.1, 95%CI 1.0-1.2), increasing Hispanic population (every 10% increase OR 1.1, 1.0-1.2), and income per household (per $10,000 OR 1.2, 1.1-1.3). Conclusions: Hospitals achieving PSC designation are located in more affluent and densely populated areas with higher population of Hispanic residents.

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Abstract T MP54: Hospital Characteristics Associated with Advanced Primary Stroke Center Designation

Stroke ◽

10.1161/str.45.suppl_1.tmp54 ◽

2014 ◽

Vol 45 (suppl_1) ◽

Author(s):

Catherine McDonald ◽

Steven Cen ◽

Lucas Ramirez ◽

William J Mack ◽

Nerses Sanossian

Keyword(s):

Systems Of Care ◽

Improve Patient Care ◽

Federal State ◽

National Guidelines ◽

American Hospital ◽

American Hospital Association ◽

Community Provider ◽

Primary Stroke Center ◽

Stroke Center ◽

Hospital Characteristics

Background: Organized stroke systems of care, including accreditation of hospitals as primary stroke centers (PSC), are meant to improve patient care and compliance with national guidelines. Nationwide, less than a third of eligible hospitals have achieved advanced certification in stroke. We aimed to characterize hospital factors associated with achievement of stroke center certification. Methods: We utilized the 2011 American Hospital Association survey to obtain data on hospital characteristics. Only hospitals with ≥ 25 beds and 24-hour emergency departments were evaluated. The Joint Commission (TJC), Healthcare Facilities Accreditation Program and DNV Healthcare websites were used to determine certification status of each hospital as a primary stroke center. All comprehensive SC were considered as PSC. Factors found to be associated with achievement of certification (P<0.010) were evaluated by logistic regression to determine a final model of independent association. Results: Of the 3696 hospitals to complete the survey, 3069 fulfilled study criteria, including 908 PSC (31%) and 2161 non-PSC. PSC were larger (mean 354 vs. 136 beds), had busier EDs (56,000 vs. 24,000 visits/year), were more often affiliated with ACGME residency programs (43% vs. 14%), AMA medical schools (51% vs. 21%), TJC-accreditation (95% vs. 65%), inpatient neurological services (94% vs. 46%) and trauma centers (55% VS 38%); and were less likely to be governmental (Federal/State/County 10% vs. 26%) and designated sole community provider (1% vs 9%). Independent hospital characteristics associated with PSC certification were TJC accreditation (OR 3.5, 95%CI 2.4-5.0), sole community provider (OR 0.22, 0.10-0.47), hospital type (governmental vs. non 0.61, 0.44-0.84), increasing size (per quartile in number of beds OR 2.5, 2.1-3.1) and neurological services (OR 3.2, 2.4-4.6). Conclusions: PSC hospitals are larger non-governmental hospitals with availability of neurological services. Increasing the low numbers of governmental (i.e. County or State) hospital achievement of PSC may be a potential area of focus.

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Information technology to support patient engagement: where do we stand and where can we go?

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocx043 ◽

2017 ◽

Vol 24 (6) ◽

pp. 1088-1094 ◽

Cited By ~ 22

Author(s):

Daniel M Walker ◽

Cynthia J Sieck ◽

Terri Menser ◽

Timothy R Huerta ◽

Ann Scheck McAlearney

Keyword(s):

Information Technology ◽

Health Information Technology ◽

Health Information ◽

Patient Engagement ◽

Ontology Mapping ◽

Hospital System ◽

American Hospital ◽

American Hospital Association ◽

Ordered Logit Model ◽

Hospital Characteristics

Abstract Objective Given the strong push to empower patients and make them partners in their health care, we evaluated the current capability of hospitals to offer health information technology that facilitates patient engagement (PE). Materials and Methods Using an ontology mapping approach, items from the American Hospital Association Information Technology Supplement were mapped to defined levels and categories within the PE Framework. Points were assigned for each health information technology function based upon the level of engagement it encompassed to create a PE-information technology (PE-IT) score. Scores were divided into tertiles, and hospital characteristics were compared across tertiles. An ordered logit model was used to estimate the effect of characteristics on the adjusted odds of being in the highest tertile of PE-IT scores. Results Thirty-six functions were mapped to specific levels and categories of the PE Framework, and adoption of each item ranged from 23.5 to 96.7%. Hospital characteristics associated with being in the highest tertile of PE-IT scores included medium and large bed size (relative to small), nonprofit (relative to government nonfederal), teaching hospital, system member, Midwest and South regions, and urban location. Discussion Hospital adoption of PE-oriented technology remains varied, suggesting that hospitals are considering how technology can create partnerships with patients. However, PE functionalities that facilitate higher levels of engagement are lacking, suggesting room for improvement. Conclusion While hospitals have reached modest levels of adoption of PE technologies, consistent monitoring of this capacity can identify opportunities to use technology to facilitate engagement.

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The Relationship between Hospital Characteristics and the Cost of Hospital Care

Health Services Management Research ◽

10.1177/095148480001300406 ◽

2000 ◽

Vol 13 (4) ◽

pp. 256-263 ◽

Cited By ~ 3

Author(s):

Patrick Asubonteng Rivers ◽

Sejong Bae

Keyword(s):

Hospital Care ◽

American Hospital ◽

American Hospital Association ◽

Annual Survey ◽

Hospital Executives ◽

The Cost ◽

The Relationship ◽

Hospital Characteristics

This article examines the relationship between hospital characteristics and costs of hospital care, using the 1991 American Hospital Association Annual Survey of Hospitals. The results discussed herein have implications for hospital executives, researchers and policymakers.

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Abstract 16216: Emergency Department Crowding Does Not Predict Door-In-Door-Out Time for Acute Myocardial Infarction Patients Transferred for Acute Coronary Intervention

Circulation ◽

10.1161/circ.130.suppl_2.16216 ◽

2014 ◽

Vol 130 (suppl_2) ◽

Author(s):

Michael J Ward ◽

Olesya Baker ◽

Jeremiah Schuur

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Emergency Department ◽

Waiting Time ◽

Coronary Intervention ◽

Quality Data ◽

American Hospital ◽

American Hospital Association ◽

Ed Crowding ◽

Hospital Characteristics

Introduction: Emergency department (ED) crowding and prolonged length of stay (LOS) are associated with lower quality care. Patients with acute myocardial infarction (AMI) should be transferred to percutaneous coronary intervention capable facilities within 45 minutes of arrival. Hypothesis: Increased ED crowding, as measured by ED LOS, is correlated with longer AMI door-in-door-out (DIDO) times at transferring EDs. Methods: We analyzed the Centers for Medicare and Medicaid Services (CMS) quality data. The outcome, DIDO, was CMS measure OP-3b, ED Median Time to Transfer a Patient with AMI for Acute Coronary Intervention . OP-3b data included hospitals with ≥10 AMI cases annually. To measure ED crowding, we used the CMS ED timeliness measures: discharged LOS, admitted LOS, boarding, and waiting time to be seen by a provider. Our primary measure of interest was ED-1: Median ED LOS for Admitted Patients . We analyzed bivariate associations between DIDO and ED timeliness measures. We used linear regression to evaluate the contribution of hospital characteristics from the American Hospital Association survey (academic, trauma, rural, ED volume) to DIDO. Results: Data were available for 405 out of 4,129 hospitals for the CMS DIDO measure (934 had <10 cases). These facilities were primarily non-academic (99%), non-trauma centers (65%), in metro locations (68.6%). Median DIDO was 54 minutes (IQR 42, 68). Increased DIDO time was associated with longer admitted LOS, and boarding times, but not waiting time or discharged LOS. Mean ED admitted LOS was different between hospitals with average DIDO <45 versus those ≥45 minutes (259 vs. 283 minutes; p=0.008). After adjusting for hospital characteristics, longer ED admitted LOS at referring facilities [coefficient, 0.078 (95% CI 0.042, 0.113); p<0.001] was associated with DIDO. A 13 minute increase in ED admitted LOS was associated with a 1 minute increase in DIDO. Among hospital characteristics only rural status was associated with longer DIDO. Conclusions: Among AMI patients presenting to U.S. EDs, we found that ED crowding has a small but operationally insignificant effect on AMI DIDO times. EDs have found ways to facilitate timely transfer of AMI patients despite crowding.

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Telepsychiatry adoption across hospitals in the United States: a cross-sectional study

BMC Psychiatry ◽

10.1186/s12888-021-03180-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zhong Li ◽

Sayward E. Harrison ◽

Xiaoming Li ◽

Peiyin Hung

Keyword(s):

Continuity Of Care ◽

Psychiatric Care ◽

The United States ◽

Psychiatric Services ◽

Linear Regression Models ◽

Cross Sectional ◽

Promising Alternative ◽

American Hospital ◽

American Hospital Association ◽

Inpatient Psychiatric Care

Abstract Background Access to psychiatric care is critical for patients discharged from hospital psychiatric units to ensure continuity of care. When face-to-face follow-up is unavailable or undesirable, telepsychiatry becomes a promising alternative. This study aimed to investigate hospital- and county-level characteristics associated with telepsychiatry adoption. Methods Cross-sectional national data of 3475 acute care hospitals were derived from the 2017 American Hospital Association Annual Survey. Generalized linear regression models were used to identify characteristics associated with telepsychiatry adoption. Results About one-sixth (548 [15.8%]) of hospitals reported having telepsychiatry with a wide variation across states. Rural noncore hospitals were less likely to adopt telepsychiatry (8.3%) than hospitals in rural micropolitan (13.6%) and urban counties (19.4%). Hospitals with both outpatient and inpatient psychiatric care services (marginal difference [95% CI]: 16.0% [12.1% to 19.9%]) and hospitals only with outpatient psychiatric services (6.5% [3.7% to 9.4%]) were more likely to have telepsychiatry than hospitals with neither psychiatric services. Federal hospitals (48.9% [32.5 to 65.3%]), system-affiliated hospitals (3.9% [1.2% to 6.6%]), hospitals with larger bed size (Quartile IV vs. I: 6.2% [0.7% to 11.6%]), and hospitals with greater ratio of Medicaid inpatient days to total inpatient days (Quartile IV vs. I: 4.9% [0.3% to 9.4%]) were more likely to have telepsychiatry than their counterparts. Private non-profit hospitals (− 6.9% [− 11.7% to − 2.0%]) and hospitals in counties designated as whole mental health professional shortage areas (− 6.6% [− 12.7% to − 0.5%]) were less likely to have telepsychiatry. Conclusions Prior to the Covid-19 pandemic, telepsychiatry adoption in US hospitals was low with substantial variations by urban and rural status and by state in 2017. This raises concerns about access to psychiatric services and continuity of care for patients discharged from hospitals.

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