The interaction between high-level electronic medical record adoption and hospitalist staffing levels: A focus on value-based purchasing

Abstract

Objective

This study sought to understand the relationship of hospital performance with high-level electronic medical record (EMR) adoption, hospitalists staffing levels, and their potential interaction.

Materials and methods

We evaluated 2,699 non-federal, general acute hospitals using 2016 data merged from four data sources. We performed ordinal logistic regression of hospitals’ total performance score (TPS) on their EMR capability and hospitalists staffing level while controlling for other market- and individual-level characteristics.

Results

Hospitalists staffing level is shown to be positively correlated with TPS. High-level EMR adoption is associated with both short-term and long-term improvement on TPS. Large, urban, non-federal government hospitals, and academic medical centers tend to have lower TPS compared to their respective counterparts. Hospitals belonging to medium- or large-sized healthcare systems have lower TPS. Higher registered nurse (RN) staffing level is associated with higher TPS, while higher percentage of Medicare or Medicaid share of inpatient days is associated with lower TPS.

Discussion

Although the main effects of hospitalists staffing level and EMR capability are significant, their interaction is not, suggesting that hospitalists and EMR act through separate mechanisms to help hospitals achieve better performance. When hospitals are not able to invest on both simultaneously, given financial constraints, they can still reap the full benefits from each.

Conclusion

Hospitalists staffing level and EMR capability are both positively correlated with hospitals’ TPS, and they act independently to bolster hospital performance.

Keywords

electronic medical record health information technology hospitalists hospital performance value-based purchasing

Introduction

The past decade has witnessed many changes in hospital operations management, hospital reimbursement, and the evaluation of hospital performance. These changes were intensified and significantly driven by the Patient Protection and Affordable Care Act (ACA) (2010). ACA restructured hospital payments to incentivize hospitals to be more quality-, patient-, and efficiency-driven. Another major policy that changed healthcare reimbursement is the Medicare Access and CHIP Reauthorization Act (MACRA), which is another pay-for-performance model that incentivizes physicians to focus on value, improvement, and patient experience. MACRA became effective in 2019 and overhauled the way Medicare pays physician payment system.¹ To adapt to a Value-Based Purchasing (VBP) era, health care organizations had to redesign the way they deliver health care services. Hospitals invested in key aspects of hospital operations, specifically health information technology, which changes the way organizations “document, monitor, and share information about health and care delivery.”² By 2015, around 80% of hospitals in the U.S. had basic electronic health records (EHRs).³ While the number of hospitals with basic EHR has increased over the past decade, there is a scarcity of research on the association between the adoption of high levels of health information technology (HIT) capabilities with performance on VBP.

Health information technology (HIT) has been, for decades, one of the critical elements believed to have the potential to improve patient outcomes and reduce waste.^4,5 HIT achieves better value; “given the large volume of transactions, the fragmented communication between providers, and the need to integrate new scientific evidence into practice, the limitations of paper-based information systems are substantial” (p. 60).⁶ In its effort to facilitate and expedite the adoption of HIT, the U.S. government implemented several initiatives. The Health Information Technology for Economic and Clinical Health (HITECH) (2009) provided financial incentives for hospitals to adopt HIT. The Centers for Medicare and Medicaid Services (CMS) provides incentives for hospitals to use EHRs meaningfully in the delivery of health care services.⁷

Despite the incentives for the adoption of HIT and the abundance of research, the findings on the relationship between information systems and quality of care are mixed and inconclusive. One study found that, among the various information systems adopted at hospitals, only the adoption of EHRs was associated with improved patient safety.⁸ While another study, conducted based on an empirical study of a national sample of U.S. hospitals, found mixed results on the relationship between basic and comprehensive EHR and quality.⁹ Another study relied on Medicare claims data from 1998 to 2005 to analyze the impact HIT had on billed hospital charges and on quality. The study found an increase in billed charges after HIT adoption but minimal changes in readmissions, mortality and drug related adverse events.¹⁰ A recent study argued that research on the shortcoming of EHRs in improving outcome measures and achieving its full potential is needed.¹¹

The findings between HIT and hospital performance on domains other than quality of care are also mixed. One study examined the impact of EHR application on labor efficiency and found no association between increased EHR applications use and labor productivity.¹² Another study, on the other hand, found a positive relationship between IT use and hospital financial performance.¹³ There are studies that found a positive relationship between IT expenditures and outsourcing levels and profitability,⁶ and a positive relationship between HIT investments and productivity and profitability.⁷ Lee at al. shed light on the disparity between IT investments and return on investment. According to Lee et al.; “health IT inputs increased by more than 210% and contributed about 6% to the increase in value-added.”¹⁴

Moreover, there is limited research on the relationship between HIT and other domains of Hospital Value-based Purchasing (HVBP). HVBP programs link Medicare payments to hospital performance on key domains, which include patient outcomes, safety, experience, and efficiency.¹⁵ Despite the impact HVBP has on hospitals, the relationship between hospital characteristics and performance on HVBP domains remains unclear.¹⁶ There are two key variables that influence the link between HITs and performance and the ability of HITs to attain the expected benefits: the capabilities of HIT adopted, and the healthcare staff who uses these capabilities. It is argued that “IT is a tool that influences performance based largely on how it is used, and without a concerted and thoughtful approach to managing how IT is used to improve performance, it will not achieve the potential quality and efficiency gains.”¹⁷

The percentage of Medicare patients overseen by hospitalists has steadily increased over the years.¹⁸ Hospitalists emerged as a hospital-based specialty in the 1990s during a period of time when hospitals experimented with mechanisms and innovations to improve quality and reduce cost.¹⁸ Hospitalists manage patients during their inpatient stay and replace the patient’s primary care physician until the patient is discharged from the hospital.¹⁹ Despite the general understanding that professional fees do not generally cover the costs for hospitalists programs,¹⁹ hospitalists continue to increase in numbers because the perceived benefits outweigh the cost.

Hospitalists coordinate care for patients, order tests, provide treatments, and perform many other tasks to manage inpatient care for patients. Since hospitalists take over the role of primary care physicians in hospitals, they play a key role in care coordination and communication and are thus key users of EHRs in hospitals. According to Tipping et al., EHRs account for 34% of hospitalists’ time.²⁰ Dalal and Rogers argue that “hospitalists are uniquely qualified to drive EHR design and implementation to improve patient safety and quality of care.”²¹ One study assessed the combined effect of electronic health record (EHR) adoption and hospitalist care on the length of stay (LOS) of Medicare beneficiaries admitted for respiratory disease. They found substantial reduction in LOS associated with hospitalist care in hospitals with incomplete EHR adoption compared to hospitals with complete EHR adoption.²²

In this study, we aim at understanding the relationship of hospital performance with EHR capabilities and hospitalists staffing levels. Specifically, we focus on the determinants of hospital performance on HVBP Total Performance Scores (HVBP TPS) by examining high-level EHR adoption, hospitalists staffing levels, and their potential interaction. TPS is a key measure of hospital performance that impacts hospital reimbursement under the VBP program. According to CMS (2020), TPS is a weighted score based on the following four domains: clinical outcomes, patient safety, person and community engagement, and efficiency and cost reduction. CMS withholds 2% of Medicare payments for hospitals. Based on how well hospitals perform compared to other hospitals and how much they improve their scores, they receive a penalty or bonus of the redistributed withheld payments.²³

Methods

Data sources and sample

Four data sources were merged to construct the final sample of this study. Hospital Total Performance Scores (TPS) for 2016 was obtained from the CMS Hospital Compare database. Unemployment rate was obtained from the 2016 Area Health Resource File. Hospital level data were obtained from the 2016 American Hospital Association (AHA) Annual Survey Database. AHA conducts an annual survey of hospitals in the United States that provides a definitive source for aggregate hospital data and trend analysis. AHA Database includes information on hospital operations, staffing, beds, service lines and facilities, utilization, and technology, among other structural and operational data. The data is reported annually by more than 6,200 hospitals and 400 health care systems in the United States. EHR implementation data were from the Healthcare Information and Management Systems Society (HIMSS) Analytics Database. HIMSS survey is conducted by the Dorenfest Institute annually. It tracks HIT used in hospitals and integrated healthcare delivery networks across the country and includes questions about a wide variety of HIT functionalities and the timing of technology adoption.

Federal public hospitals and hospitals with missing TPS scores or data on any variable included in the model were excluded from our study. Federal hospitals, specifically those that belong to the Veteran Affairs, do not receive Medicare payment and therefore are not impacted by Total Performance Scores. The final sample consisted of 2,699 non-federal general acute hospitals.

Dependent variable

In 2016, TPS was measured as a weighted score of the following domains: processes of care outcomes, patient outcomes, patient experience, and efficiency and cost reduction. The weight for clinical process of care was 10%, patient experience 25%, outcomes 40%, and efficiency 25%. TPS scores for 2016 were obtained from CMS.

Independent variables

The independent variables of this study are hospitalists staffing level (obtained from the AHA Annual Survey Database) and EHR transition type (defined based on EHR implementation data from the HIMSS Analytics Database). Hospitalists staffing level is calculated as full-time equivalent (FTE) hospitalists divided by the number of general staffed beds and discretized into three levels from low to high. The HIMSS Analytics Electronic Medical Record Adoption Model (EMRAM) defines eight stages of EHR adoption, ranging from Stage 0 where none of the three ancillaries, laboratory, pharmacy, and radiology/cardiology information systems is installed to Stage 7 where complete EHR, external health information exchange (HIE), and data analytics, governance, disaster recovery, and privacy and security are in place. Stage 6 is the adoption stage prior to attaining the final Stage 7 and an important indicator that an organization has successfully addressed many industry transformations and can deliver high quality patient care with an interoperable electronic health record (EHR) in place.^24,25 The HIMSS validates and certifies Stages 6 and 7 and since our study focuses on high-level EHR adoption, therefore we use whether a hospital has achieved Stage 6 or Stage 7 as an indicator of its EHR capability.^a In our final data set, 46.6% and 6.2% of the hospitals were Stage 6 and Stage 7 certified in 2016, respectively. In order to capture both the short-term and long-term effect of EHR adoption, we classify the hospitals into three types of EHR capability transition by considering their EHR capability in two years, 2012 and 2016.:

Type 1 – “Low-level adopters”: hospitals that didn’t achieve Stage 6 or higher in 2012 nor in 2016;

Type 2 – “Switchers”: hospitals that didn’t achieve Stage 6 or higher in 2012, but achieved it in 2016;

Type 3 – “High-level adopters”: hospitals that had achieved HIMSS Stage 6 or higher in 2012 and 2016.^b

In the final sample, there are 1,442 (53.4%) Type 1 hospitals, 873 (32.3%) Type 2 hospitals, and 384 (14.2%) Type 3 hospitals.

Control variables

For market‐level variables, we include unemployment rate and competition (1-Herfindahl‐Hirschman Index (HHI)) that measures market concentration. HHI is calculated by summing the square of the market share of total admissions in a county for each hospital in the county. We use competition instead of HHI for the ease of interpretation.

To control for individual hospital heterogeneity, we include the following organizational‐level characteristics: location (urban vs. rural), ownership (not‐for‐profit, for‐profit, or local public hospitals), size, teaching and academic medical center (AMC) status, whether a hospital belongs to a system and system size, whether a hospital is Magnet recognized, Medicare and Medicaid share of total inpatient days, registered nurse (RN) staffing level (registered nurses FTE divided by the total facility inpatient days) discretized into three levels from low to high, and occupancy rate (calculated as the number of inpatient days divided by 365*the number of general beds). All these hospital-level variables were obtained from the AHA Annual Survey Database.

We also control for the status of ANCC Magnet Recognition Program® since it is an indicator of organizational leadership and culture that fosters high‐quality care.^c Magnet hospitals have attributes that are linked to better patient outcomes.²⁶ These attributes include transformational leadership, decentralized and dynamic structure, greater nurse autonomy and empowerment, and commitment to quality.²⁷ Moreover, we control for system membership and size since both factors are associated with EHR adoption.²⁸ Larger hospitals and those that belong to systems have access to more resources than those who don’t.

Table 1 provides summary statistics of all hospitals and breakdown by EHR capability transition type. We performed the Kruskal–Wallis test on the continuous variables and the Chi-squared test on the categorical variables to determine whether the three types of hospitals are different in terms of these variables individually. The Kruskal–Wallis test is chosen, instead of ANOVA, is because the normality and equal variance assumptions are not satisfied for some continuous variables. The p value of these tests are shown in the last column of Table 1. A p value of less than 0.05 indicates that a statistically significant difference exists. For example, the average number of hospitalists per general bed is significantly different among the three types of hospitals, with Type 1 (i.e., low-level adopters) having the lowest while Type 3 (i.e., high-level adopters) having the highest and. Among the control variables, location (urban vs. rural), RN staffing level, ownership, teaching hospital status, AMC status, system membership, size, Magnet status, and occupancy rate are significantly different among the three types; while unemployment rate, competition, and Medicare and Medicaid shares of inpatient days are not.

Table 1.

Summary statistics of all hospitals and hospitals by transition type.

Variable	Mean (SD) or count (percentage of sample)				P value of Kruskal–Wallis Test
Variable	All hospitals	Type 1 – low-level adopters	Type 2 – switchers	Type 3 – high-leveladopters	P value of Kruskal–Wallis Test
Dependent variable:
TPS	34.30 (11.19)	33.59 (11.43)	34.45 (10.58)	36.65 (11.35)	<0.001
Independent variables:
Number of hospitalists per general bed:	0.17 (0.44)	0.13 (0.29)	0.19 (0.32)	0.25 (0.90)	<0.001
No hospitalists (=0)	933 (34.6%)	565 (39.2%)	278 (31.8%)	90 (23.4%)
Low level: (0, 0.11]	590 (21.8%)	335 (23.2%)	179 (20.5%)	76 (19.8%)
Medium level: (0.11, 0.22]	593 (22.0%)	287 (19.9%)	195 (22.3%)	111 (28.9%)
High level: (>0.22)	583 (21.6%)	255 (17.7%)	221 (25.3%)	107 (27.9%)
Control variables:
Urban (binary)	2,071 (76.7%)	1,012 (70.2%)	730 (83.6%)	329 (85.7%)	<0.001
Unemployment rate	5.16 (1.74)	5.21 (1.84)	5.10 (1.59)	5.12 (1.71)	0.43
Competition (1-HHI)	0.50 (0.35)	0.50 (0.35)	0.51 (0.35)	0.51 (0.35)	0.85
RN FTE per inpatient day:	0.0096 (0.0090)	0.0097 (0.0055)	0.0093 (0.0044)	0.0100 (0.0035)	<0.001
Low level: (0, 0.0075]	908 (33.6%)	521 (36.1%)	308 (35.3%)	79 (20.6%)
Medium level: (0.0075, 0.010]	902 (33.4%)	444 (30.8%)	308 (35.3%)	150 (39.1%)
High level: (>0.010)	889 (32.9%)	477 (31.1%)	257 (29.4%)	155 (40.4%)
Medicare share of inpatient days (%)	51.13 (12.79)	51.33 (13.23)	51.19 (12.39)	50.36 (12.00)	0.09
Medicaid share of inpatient days (%)	21.16 (11.79)	21.48 (12.49)	21.04 (11.17)	20.26 (10.38)	0.55
Ownership:					<0.001
Not-for-profit	1,787 (66.2%)	836 (58.0%)	619 (70.9%)	332 (86.5%)
For-profit	567 (21.0%)	388 (26.9%)	156 (17.9%)	23 (6.0%)
Government (non-federal)	345 (12.8%)	218 (15.1%)	98 (11.2%)	29 (7.6%)
Teaching hospital	814 (30.2%)	378 (26.2%)	275 (31.5%)	161 (41.9%)	<0.001
Academic medical center	217 (8.0%)	71 (4.9%)	87 (10.0%)	59 (15.4%)	<0.001
System membership:					<0.001
Not belonging to a system	651 (24.1%)	425 (29.5%)	159 (18.2%)	67 (17.4%)
Belong to a small system (<= 40 members)	1,409 (52.2%)	660 (45.8%)	484 (55.4%)	265 (69.0%)
Belong to a medium system (40–120 members)	367 (13.6%)	206 (14.3%)	112 (12.8%)	49 (12.8%)
Belong to a large system (>120 members)	272 (10.1%)	151 (10.5%)	118 (13.5%)	3 (0.8%)
Hospital size:					<0.001
Small (<100 beds)	697 (25.8%)	453 (31.4%)	179 (20.5%)	65 (16.9%)
Medium (100–400 beds)	1,571 (58.2%)	841 (58.3%)	512 (58.7%)	218 (56.8%)
Large (>400 beds)	431 (16.0%)	148 (10.3%)	182 (20.8%)	101 (26.3%)
Magnet recognized	309 (11.4%)	96 (6.7%)	124 (14.2%)	89 (23.2%)	<0.001
Occupancy rate (# inpatient days/(# beds*365))	0.57 (0.18)	0.54 (0.18)	0.60 (0.17)	0.63 (0.17)	<0.001
Sample size	2,699	1,442 (53.4%)	873 (32.3%)	384 (14.2%)

Analysis

We perform linear regression model of TPS on the independent and control variables discussed above. We discretize the independent and control variables that are highly skewed, and centered and scaled the rest of them. We include an interaction term between hospitalists staffing level and EHR transition type in the model to examine whether there is synergy between the two in terms of their association with TPS.

Results

The result of the linear regression is shown in Table 2, where parameter estimates, standard errors, and p values are provided. The p value of the overall model F statistic is less than 0.001 and the adjusted R-squared is 0.155. The diagnostic plots do not suggest any severe violations of linear regression assumptions. The variance inflation factors (VIFs) of all variables (including the interaction terms) are less than 5, which indicates that multicollinearity is not a concern. The maximum value of Cook’s distance is 0.014 with an average of 0.00038. Therefore, we do not identify any observations to be highly influential.

Table 2.

Linear regression model of TPS on hospitalists staffing level and EMR transition type.

Variable	Estimate	Std. Error	P value
Independent variables:
Number of hospitalists per general bed:
No hospitalists (=0)	(reference level)
Low level: (0, 0.11]	−0.121	0.721	0.867
Medium level: (0.11, 0.22]	0.540	0.766	0.481
High level: (>0.22)	2.459	0.800	0.002**
EMR transition type:
Type 1 – Low-level adopters	(reference level)
Type 2 – Switchers	1.724	0.778	0.027*
Type 3 – High-level adopters	4.030	1.191	0.001**
Interaction terms:
Type 2* Low level of hospitalists	1.060	1.230	0.389
Type 2* Medium level of hospitalists	−0.109	1.231	0.929
Type 2* High level of hospitalists	−0.156	1.221	0.898
Type 3* Low level of hospitalists	−0.919	1.762	0.602
Type 3* Medium level of hospitalists	0.270	1.652	0.870
Type 3* High level of hospitalists	−1.260	1.669	0.451
Control variables:
Urban (binary)	−2.994	0.553	0.000**
Unemployment rate	0.070	0.115	0.541
Competition (1-HHI)	0.005	0.576	0.994
RN FTE per inpatient day:
Low level: (0, 0.0075]	(reference level)
Medium level: (0.0075, 0.010]	1.193	0.513	0.020*
High level: (> 0.010)	2.423	0.598	0.000**
Medicare share of inpatient days (%)	−0.144	0.021	0.000**
Medicaid share of inpatient days (%)	−0.083	0.023	0.000**
Ownership:
Not-for-profit	(reference level)
Government (non-federal)	−2.247	0.662	0.001**
For-profit	−0.854	0.709	0.228
Teaching hospital (binary)	−1.036	0.483	0.032*
Academic medical center (binary)	−2.814	0.943	0.003**
Hospital size:
Small (< 100 beds)	7.471	0.883	0.000**
Medium (100–400 beds)	3.257	0.679	0.000**
Large (> 400 beds)	(reference level)
System membership:
Not belonging to a system	(reference level)
Belong to a small system (<= 40 members)	0.406	0.532	0.446
Belong to a medium system (40–120 me mbers)	−2.547	0.735	0.001**
Belong to a large system (>120 members)	−2.304	1.017	0.024*
Magnet recognized	0.834	0.693	0.229
Occupancy rate (# inpatient days / (# beds*365))	−1.242	1.508	0.410

Significance levels: **p-value < 0.01, *p-value < 0.05, p-value < 0.1.

High hospitalists staffing level is shown to be positively correlated with TPS. Comparing to low-level adopters (i.e., Type 1), switchers (i.e., Type 2) and high-level adopters (i.e., Type 3) both have higher TPS. The coefficient estimates of switchers and high-level adopters are 1.724 and 4.030, respectively (where the difference is also statistically significant), which means that hospitals that had already achieved Stage 6 or higher in 2012 have even higher TPS than hospitals achieving the status later. It suggests long term benefits of high-level EHR adoption on improving TPS. We also included full interaction terms between hospitalists staffing level and EHR transition type. They all turn out to be insignificant and therefore we do not find evidence of an interaction effect. The results also show that urban hospitals, non-federal government hospitals, teaching hospitals and academic medical centers tend to have lower TPS compared to their respective counterparts. Large hospitals have lower TPS compared to small- and medium-sized hospitals. Hospitals belonging to medium- or large-sized systems have lower TPS compared to hospitals belonging to small-sized systems or not belonging to a system. In addition, higher RN staffing level is associated with higher TPS, while higher percentage of Medicare or Medicaid share of inpatient days is associated with lower TPS.

Discussion

Financial penalties and incentives have resulted in progress in adopting basic EHRs by hospitals in the U.S.²⁹ According to Alder-Milstein et al., in 2015, around 80% of hospitals in the United States had at least a basic EHRs.³ Nevertheless, there remain questions on the level of capabilities needed for significant hospital performance gains and the interaction between the adoptions of high levels of EHR capabilities with other dimensions of hospital operations such as physician staffing levels. The purpose of this paper is to examine the association between high-level EHR adoption and hospitalists staffing levels with hospital performance on VBP TPS. According to our findings, hospitals achieving HIMSS EHR Stage 6 or higher since 2012, i.e., high-level adopters, and hospitals that switched from lower stages to Stage 6 or higher between 2012 and 2016, i.e., switchers, both had significantly higher VBP-TPS than hospitals still not achieving Stage 6 or higher in 2016. Moreover, high-level adopters had significantly higher TPS than switchers. It suggests that high-level EHR capability not only helps to improve hospital performance in the short term (i.e., when comparing Type 1 and Type 2 hospitals), but also helps in the long run (i.e., when comparing Type 2 and Type 3 hospitals). Higher staffing levels of both hospitalists and RNs are associated with higher performance scores. We do not find evidence of an interaction effect between hospitalists staffing level and high-level EHR adoption.

Health care organizations face barriers that limit the scope of health information technology adoption. Perhaps one of the main barriers to adopting HIT is the cost.³⁰ However, the financial penalties associated with low performance on VBP-TPS could potentially outweigh the cost of HIT. It is stated that “while EHR adoption has increased steadily since 2010, it is unclear how providers that have not yet adopted will fare now that federal incentives have converted to penalties.”²⁹ According to our findings, hospitals with higher stages of HIMSS EHR since 2012 had the highest TPS, while those with lower levels of EHR did not do well. This finding is consistent with the maturity model first proposed by Richard L. Nolan in 1973.³¹ According to the maturity model, organizations progress through four stages of IT adoption: (1) initiation, (2) contagion, (3) data administration, and (4) maturity. HIMSS’s EHR Adoption Model classifies hospitals into stages based on the HIT adoption maturity.³² High-level adopters have reached higher maturity levels in the adoption of information technology and, therefore, capitalize on the high HIT capabilities that they possess and are more likely to have integrated HIT in most of their processes.

While various scholars have discussed the barriers to the adoption of EHR, few have examined the factors needed to effectively adopt and use EHR.³³ In our effort to further the understanding of factors that might influence the effectiveness of EHR in hospitals, we examined how the interaction between hospitalist staffing levels and EHR capabilities is associated with hospital performance. One study estimated that hospitalists spend 34% of their time on EHR use. More importantly, hospitalists with heavier workload spent less time using EHR than those with lower workload.²⁰ Given the important role that hospitalists play in caring for hospital patients, they, in addition to nurses, are likely to influence the effectiveness of EHR. While according to our findings, the interaction between EHR capabilities and hospitalists staffing level is not a significant predictor of VBP TPS, more research is needed to understand the relationship between hospital medical staffing levels and HIT capabilities.

Limitations

This study has a few limitations. The AHA Annual Survey Database and the HIMSS Analytics Database consist of self-reported survey data. The HIMSS EMRAM defines eight stages of EHR adoption. We consider only Stages 6 and 7 in this study because HIMSS certifies these two stages. A comprehensive matching of hospital EHR capability to all eight stages of EMRAM will enable the examination of early stage EHR adoption and its relationship to hospital performance. Moreover, our cross-sectional model lacks the ability to make causal inference. Longitudinal analysis is needed to uncover possible causal relationship between EHR adoption and improved hospital performance.

Conclusion

Hospitals have been investing in key aspects of hospital operations in order to enhance quality of care and boost overall hospital performance in the VBP era. Health information technology, in particular EHRs, and physician staffing are two essential facets that hospitals focus on. Our study shows that high-level EHR and higher hospitalists staffing level are associated with higher TPS, and they boost hospital performance via different mechanisms. Further research can delve into different subdomains of TPS and examine whether EHR and hospitalists have synergy in enhancing any particular domains of TPS, and expand to types of HIT other than EHRs to explore their interplay with physician staffing and joint impact on hospital performance.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Kate Jiayi Li

Notes

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