Association of Financial Factors and Telemedicine Adoption for Heart Attack and Stroke Care Among Rural and Urban Hospitals: A Longitudinal Study.

Introduction: To examine trends in telemedicine adoption for stroke and cardiac care among U.S. hospitals, specifically associations between hospital financial indicators and adoption of these telemedicine services. Methods: This is a retrospective analysis of data from the Health Information Management and System Society Dorenfest Database and Healthcare Cost Report Information System from 2012 to 2017. We used a pooled ordinary least squares model and reported results as average marginal effects (AMEs). Results: The number of hospitals with stroke or cardiac telemedicine services in urban and rural areas increased through our study period from 153 (7.30%) to 407 (19.42%) and from 127 (6.31%) to 331 (16.45%), respectively. In rural hospitals, being a for-profit hospital (AME = -10.49, 95% confidence interval [CI] = -14.01 to -6.98) and having an increase in Medicare inpatient mix (AME = -0.31, 95% CI = -0.42 to -0.20) were associated with the probability of telemedicine adoption for heart attack and stroke care. A couple of nonfinancial variables included in the model also were associated with adoption, specifically having one more licensed bed (AME = -0.02, 95% CI = -0.04 to -0.00) and higher number of emergency department visits (AME = 5.64, 95% CI = 2.83 to 7.20). In urban hospitals, being a for-profit hospital (AME = -8.94, 95% CI = -11.76 to -6.11) and having a higher total margin (AME = 0.17, 95% CI = 0.08 to 0.26) were associated with the probability of telemedicine adoption for heart attack and stroke care. Two nonfinancial variables also were statistically significant: having one more licensed bed (AME = 0.01, 95% CI = 0.041 to 0.02) and being closer to another telemedicine hospital (AME = 0.81, 95% CI = -1.62 to 0.01). Discussions: Telemedicine adoption rate for cardiac and stroke care has increased significantly in recent years. Financial status may be a bigger driver of adoption for urban hospitals than rural hospitals.

Do Hospitals Providing Telehealth in Emergency Departments Have Lower Emergency Department Costs?

Background: Since 2003, the University of Mississippi Medical Center has operated a robust telehealth emergency department (ED) network, TelEmergency, which enhances access to emergency medicine-trained physicians at participating rural hospitals. TelEmergency was developed as a cost-control measure for financially constrained rural hospitals to improve access to quality, emergency care. However, the literature remains unclear as to whether ED telehealth services can be provided at lower costs compared with traditional in-person ED services. Introduction: Our objective was to empirically determine whether TelEmergency was associated with lower ED costs at rural hospitals when compared with similar hospitals without TelEmergency between 2010 and 2017. Materials and Methods: A panel of data for 2010-2017 was constructed at the hospital level. Hospitals with TelEmergency (n = 14 hospitals; 112 hospital-years) were compared with similar hospitals that did not use TelEmergency from Arkansas, Georgia, Mississippi, and South Carolina (n = 102; 766 hospital-years), matched using Coarsened Exact Matching. The relationship between total ED costs and treatment (e.g., participation in TelEmergency) was predicted using generalized estimating equations with a Poisson distribution, a log link, an exchangeable error term, and robust standard errors. Results: After controlling for ownership type, critical access hospital status, year, and size, TelEmergency was associated with an estimated 31.4% lower total annual ED costs compared with similar matched hospitals that did not provide TelEmergency. Conclusions: TelEmergency utilization was associated with significantly lower total annual ED costs compared with similarly matched hospitals that did not utilize TelEmergency. These findings suggest that access to quality ED care in rural communities can occur at lower costs.

Capital Expenditures Increased at Rural Hospitals That Merged Between 2012 and 2015.

EXECUTIVE SUMMARY: The number of rural hospital mergers has increased substantially in recent years. A commonly reported reason for merging is to increase access to capital. However, no empirical evidence exists to show whether capital expenditures increased at rural hospitals after a merger. We used a difference-in-differences approach to determine whether total capital expenditures changed at rural hospitals after a merger. The comparison group (rural hospitals that did not merge during the 2012 through 2015 study period) was weighted using inverse probability of treatment weights. The key outcome measure was logged total capital expenditures.Merging resulted in a 26% increase in capital expenditures and also was associated with a significant improvement in plant age. The postmerger improvement in plant age may have been partially attributable to merger-related accounting changes and partially attributable to increased capital expenses, possibly on long-term asset renovations and replacement.These findings suggest that through mergers, rural hospital board members and executives who have accepted or are considering a merger may improve a hospital's ability to increase capital expenditures. Further, increased capital investments in rural hospitals may be an important signal to the community that the acquirer intends to keep the rural hospital open and continue providing some volume and level of services within the community. Future research should determine how capital is spent after a merger.

Rural Health Care Costs: Are They Higher and Why Might They Differ from Urban Health Care Costs?

Do health care costs differ between rural and urban populations, and if so, why might that be? Rural Americans are more vulnerable than their urban counterparts, which could lead us to suspect rural health care costs are higher. However, the answer may differ depending on how costs are measured and who is paying.

United States' Nursing Home Finances: Spending, Profitability, and Capital Structure.

Little is known about nursing home (NH) financial status in the United States even though most NH care is publicly funded. To address this gap, this descriptive study used 2019 Medicare cost reports to examine NH revenues, expenditures, net income, related-party expenses, expense categories, and capital structure. After a cleaning process for all free-standing NHs, a study population of 11,752 NHs was examined. NHs had total net revenues of US$126 billion and a profit of US$730 million (0.58%) in 2019. When US$6.4 billion in disallowed costs and US$3.9 billion in non-cash depreciation expenses were excluded, the profit margin was 8.84 percent. About 77 percent of NHs reported US$11 billion in payments to related-party organizations (9.54% of net revenues). Overall spending for direct care was 66 percent of net revenues, including 27 percent on nursing, in contrast to 34 percent spent on administration, capital, other, and profits. Finally, NHs had long-term debts that outweighed their total available financing. The study shows the value of analyzing cost reports. It indicates the need to ensure greater accuracy and completeness of cost reports, financial transparency, and accountability for government funding, with implications for policy changes to improve rate setting and spending limits.

Nursing home finances associated with real estate investment trust and private equity investments.

In 2021, real estate investment trusts (REITs) and private equity (PE) held investments in 1915 (16%) and 1569 (13%) US nursing homes (NHs), respectively. We created a database of REIT and PE investments in NHs, merged it with Medicare Cost Report data (2011-2019), and used a difference-in-differences approach within an event-study framework to compare NH spending and financial performance before and after REIT or PE investment to NHs that did not receive REIT or PE investment. REIT investments were associated with higher total wages (3%), total nursing wages (3%; both logged, per resident day [PRD]), and current ratio (81%). PE investments were associated with lower net patient service revenue (7%), total expenses (7%), and total wages (8%; all logged, PRD). The impact of REIT and PE investments in NHs may vary in different market conditions, as may occur in the current environment of low, falling NH profits, potentially higher minimum staffing requirements, and rising interest rates. Therefore, it is important for stakeholders to understand the impact of these large, growing investments on the financial performance of NHs.

The Role Of Real Estate Investment Trusts In Staffing US Nursing Homes.

In 2021 real estate investment trusts (REITs) held investments in 1,806 US nursing homes. REITs are for-profit public or private corporations that invest in income-producing properties. We created a novel database of REIT investments in US nursing homes, merged it with Medicare cost report data (2013-19), and used a difference-in-differences approach within an event study framework to compare staffing before and after a nursing home received REIT investment with staffing in for-profit nursing homes that did not receive REIT investment. REIT investment was associated with average relative staffing increases of 2.15 percent and 1.55 percent for licensed practical nurses (LPNs) and certified nursing assistants (CNAs), respectively. During the postinvestment period, registered nurse (RN) staffing was unchanged, but event study results showed a 6.25 percent decrease in years 2 and 3 after REIT investment. After the three largest REIT deals were excluded, REIT investments were associated with an overall 6.25 percent relative decrease in RN staffing and no changes in LPN and CNA staffing. Larger deals resulted in increases in LPN and CNA staffing, with no changes in RN staffing; smaller deals appeared to replace more expensive and skilled RN staffing with less expensive and skilled staff.

Tele-ICUs for COVID-19: A Look at National Prevalence and Characteristics of Hospitals Providing Teleintensive Care.

PURPOSE: Amidst the COVID-19 outbreak, the use of intensive care unit telemedicine (tele-ICUs) may be one mechanism to provide patient care, particularly in rural parts of the United States. The purpose of this research was to inform hospital decision makers considering tele-ICUs, policy makers weighing immediate and longer-term funding and reimbursement decisions relative to tele-ICU care, and researchers conducting future work evaluating tele-ICUs. METHODS: We compared hospitals that reported providing teleintensive care to hospitals that reported not providing teleintensive care in the 2018 American Hospital Association Annual Survey (AHAAS). Differences between groups were tested using Pearson's chi-square (categorical variables) and t-tests (continuous variables) using 0.05 as the probability of Type 1 error. The study sample included all US short-term, acute care hospitals that responded to the AHAAS in 2018. Our key variable of interest was whether a hospital reported having any tele-ICU capabilities in the 2018 AHAAS. Other factors evaluated were ownership, region, beds, ICU beds, outpatient visits, emergency department visits, full-time employees, and whether a hospital was rural, a critical access hospital, a major teaching hospital, or part of a health system. FINDINGS: Larger, not-for-profit, nonrural, noncritical access, teaching hospitals that were part of a health system, particularly in the Midwest, were more likely to have tele-ICUs. Over one-third of hospital referral regions (HRRs) had zero hospitals with tele-ICUs, 4 had all hospitals with tele-ICU, and the median percent of hospitals with tele-ICU by HRR, weighted by outpatient visits, was 11.3%. CONCLUSIONS AND IMPLICATIONS: We found wide variation in the prevalence of tele-ICUs across HRRs and states. Future work should continue the evaluation of tele-ICU effectiveness and, if favorable, explore the variation we identified for improved access to teleintensive care.

Minimally important difference in cost savings: Is it possible to identify an MID for cost savings?

As healthcare costs continue to increase, studies assessing costs are becoming increasingly common, but researchers planning for studies that measure costs differences (savings) encounter a lack of literature or consensus among researchers on what constitutes "small" or "large" cost savings for common measures of resource use.  Other fields of research have developed approaches to solve this type of problem. Researchers measuring improvement in quality of life or clinical assessments have defined minimally important differences (MID) which are then used to define magnitudes when planning studies. Also, studies that measure cost effectiveness use benchmarks, such as cost/QALY, but do not provide benchmarks for cost differences. In a review of the literature, we found no publications identifying indicators of magnitude for costs. However, the literature describes three approaches used to identify minimally important outcome differences: (1) anchor-based, (2) distribution-based, and (3) a consensus-based Delphi methods. In this exploratory study, we used these three approaches to derive MID for two types of resource measures common in costing studies for: (1) hospital admissions (high cost); and (2) clinic visits (low cost). We used data from two (unpublished) studies to implement the MID estimation. Because the distributional characteristics of cost measures may require substantial samples, we performed power analyses on all our estimates to illustrate the effect that the definitions of "small" and "large" costs may be expected to have on power and sample size requirements for studies. The anchor-based method, while logical and simple to implement, may be of limited value in cases where it is difficult to identify appropriate anchors. We observed some commonalities and differences for the distribution and consensus-based approaches, which require further examination. We recommend that in cases where acceptable anchors are not available, both the Delphi and the distribution-method of MID for costs be explored for convergence.

For Rural Hospitals That Merged, Inpatient Charges Decreased and Outpatient Charges Increased: A Pre-/Post-Comparison of Rural Hospitals That Merged and Rural Hospitals That Did Not Merge Between 2005 and 2015.

PURPOSE: To determine whether inpatient and outpatient charges changed at rural hospitals after a merger. METHODS: Hospital mergers were derived from proprietary Irving Levin Associates data through manual review and validation. Hospital-level characteristics were derived from HCRIS, CMS Impact File Hospital Inpatient Prospective Payment System, Hospital MSA file, AHRF, and US Census data. A difference-in-differences approach was used to determine whether inpatient and outpatient charges changed at rural hospitals after a merger. The comparison group, rural hospitals that did not merge at any point during the sample period, was weighted using inverse probability of treatment weights. Key outcome measures were total inpatient and total outpatient charges (logged). FINDINGS: Hospitals that merged billed 17.73% more inpatient charges and 12.66% more outpatient charges at baseline compared to hospitals that did not merge. Our results indicate that merging was associated with a 3.04% decrease in inpatient charges (P < .001) and a 1.07% increase in outpatient charges (P = .082). Merging was also associated with a 4.38% decrease in total revenue, a 3.58% decrease in net patient revenue, and no change in total inpatient discharges or average daily census. CONCLUSIONS & IMPLICATIONS: Merging was strongly associated with a decrease in inpatient charges and somewhat associated with an increase in outpatient charges for rural hospitals. Future work could build upon this work to determine whether acquirers reduce or eliminate certain services at rural hospitals after a merger, and ultimately how changes in service delivery could impact patients in those rural communities.

Population-Level Interest and Telehealth Capacity of US Hospitals in Response to COVID-19: Cross-Sectional Analysis of Google Search and National Hospital Survey Data.

BACKGROUND: As the novel coronavirus disease (COVID-19) is widely spreading across the United States, there is a concern about the overloading of the nation's health care capacity. The expansion of telehealth services is expected to deliver timely care for the initial screening of symptomatic patients while minimizing exposure in health care facilities, to protect health care providers and other patients. However, it is currently unknown whether US hospitals have the telehealth capacity to meet the increasing demand and needs of patients during this pandemic. OBJECTIVE: We investigated the population-level internet search volume for telehealth (as a proxy of population interest and demand) with the number of new COVID-19 cases and the proportion of hospitals that adopted a telehealth system in all US states. METHODS: We used internet search volume data from Google Trends to measure population-level interest in telehealth and telemedicine between January 21, 2020 (when the first COVID-19 case was reported), and March 18, 2020. Data on COVID-19 cases in the United States were obtained from the Johns Hopkins Coronavirus Resources Center. We also used data from the 2018 American Hospital Association Annual Survey to estimate the proportion of hospitals that adopted telehealth (including telemedicine and electronic visits) and those with the capability of telemedicine intensive care unit (tele-ICU). Pearson correlation was used to examine the relations of population search volume for telehealth and telemedicine (composite score) with the cumulative numbers of COVID-19 cases in the United States during the study period and the proportion of hospitals with telehealth and tele-ICU capabilities. RESULTS: We found that US population-level interest in telehealth increased as the number of COVID-19 cases increased, with a strong correlation (r=0.948, P<.001). We observed a higher population-level interest in telehealth in the Northeast and West census region, whereas the proportion of hospitals that adopted telehealth was higher in the Midwest region. There was no significant association between population interest and the proportion of hospitals that adopted telehealth (r=0.055, P=.70) nor hospitals having tele-ICU capability (r=-0.073, P=.61). CONCLUSIONS: As the number of COVID-19 cases increases, so does the US population's interest in telehealth. However, the level of population interest did not correlate with the proportion of hospitals providing telehealth services in the United States, suggesting that increased population demand may not be met with the current telehealth capacity. Telecommunication infrastructures in US hospitals may lack the capability to address the ongoing health care needs of patients with other health conditions. More practical investment is needed to deploy the telehealth system rapidly against the impending patient surge.

Rural Hospital Mergers Increased Between 2005 and 2016-What Did Those Hospitals Look Like?

The objective of this study is to determine whether key hospital-level financial and market characteristics are associated with whether rural hospitals merge. Hospital merger status was derived from proprietary Irving Levin Associates data for 2005 through 2016 and hospital-level characteristics from HCRIS, CMS Impact File Hospital Inpatient Prospective Payment System, Hospital MSA file, AHRF, and U.S. Census data for 2004 through 2016. A discrete-time hazard analysis using generalized estimating equations was used to determine whether factors were associated with merging between 2005 and 2016. Factors included measures of profitability, operational efficiency, capital structure, utilization, and market competitiveness. Between 2005 and 2016, 11% (n = 326) of rural hospitals were involved in at least one merger. Rural hospital mergers have increased in recent years, with more than two-thirds (n = 261) occurring after 2011. The types of rural hospitals that merged during the sample period differed from nonmerged rural hospitals. Rural hospitals with higher odds of merging were less profitable, for-profit, larger, and were less likely to be able to cover current debt. Additional factors associated with higher odds of merging were reporting older plant age, not providing obstetrics, being closer to the nearest large hospital, and not being in the West region. By quantifying the hazard of characteristics associated with whether rural hospitals merged between 2005 and 2016, these findings suggest it is possible to determine leading indicators of rural mergers. This work may serve as a foundation for future research to determine the impact of mergers on rural hospitals.