JMIR MEDICAL INFORMATICS

Modi et al

Original Paper

Value of Electronic Health Records Measured Using Financial
and Clinical Outcomes: Quantitative Study
Shikha Modi1,2, MBA, PhD; Sue S Feldman2, RN, MEd, PhD; Eta S Berner2, EdD; Benjamin Schooley3, PhD; Allen
Johnston4, PhD
1The University of Alabama in Huntsville, Huntsville, AL, United States

2

The University of Alabama at Birmingham, Birmingham, AL, United States

3

Brigham Young University, Provo, UT, United States

4

Department of Information Systems, Statistics, and Management Science, The University of Alabama, Tuscaloosa, AL, United States

Corresponding Author:
Shikha Modi, MBA, PhD
The University of Alabama in Huntsville
1610 Ben Graves Dr NW
Huntsville, AL, 35816
United States
Phone: 1 2568242437
Email: ssm0031@uah.edu

Abstract
Background: The Health Information Technology for Economic and Clinical Health Act of 2009 was legislated to reduce healthcare costs, improve quality, and increase patient safety. Providers and organizations were incentivized to exhibit meaningful use
of certified electronic health record (EHR) systems in order to achieve this objective. EHR adoption is an expensive investment,
given the resources and capital that are invested. Due to the cost of the investment, a return on the EHR adoption investment is
expected.
Objective: This study performed a value analysis of EHRs. The objective of this study was to investigate the relationshipbetween EHR adoption levels and financial and clinical outcomes by combining both financial and clinical outcomes into one
conceptual model.
Methods: We examined the multivariate relationships between different levels of EHR adoption and financial and clinicaloutcomes, along with the time variant control variables, using moderation analysis with a longitudinal fixed effects model. Since
it is unknown as to when hospitals begin experiencing improvements in financial outcomes, additional analysis was conductedusing a 1- or 2-year lag for profit margin ratios.
Results: A total of 5768 hospital-year observations were analyzed over the course of 4 years. According to the results of themoderation analysis, as the readmission rate increases by 1 unit, the effect of a 1-unit increase in EHR adoption level on theoperating margin decreases by 5.38%. Hospitals with higher readmission payment adjustment factors have lower penalties.
Conclusions: This study fills the gap in the literature by evaluating individual relationships between EHR adoption levels andfinancial and clinical outcomes, in addition to evaluating the relationship between EHR adoption level and financial outcomes,
with clinical outcomes as moderators. This study provided statistically significant evidence (P<.05), indicating that there is arelationship between EHR adoption level and operating margins when this relationship is moderated by readmission rates, meaning
hospitals that have adopted EHRs could see a reduction in their readmission rates and an increase in operating margins. Thisfinding could further be supported by evaluating more recent data to analyze whether hospitals increasing their level of EHR
adoption would decrease readmission rates, resulting in an increase in operating margins. Hospitals would incur lower penalties
as a result of improved readmission rates, which would contribute toward improved operating margins.
(JMIR Med Inform 2024;12:e52524) doi: 10.2196/52524
KEYWORDS
acceptance; admission; adoption; clinical outcome; cost; economic; EHR adoption; EHR; electronic health record; finance;
financial outcome; financial; health outcome; health record; hospital; hospitalization; length of stay; margin; moderation analysis;
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Modi et almultivariate; operating margin; operating; operation; operational; profit; project management; readmission rate; readmission;
total margin; value analysis; value engineering; value management

Introduction
Overview
The Health Information Technology for Economic and Clinical
Health (HITECH) Act of 2009 was legislated to reduce healthcare costs, improve quality, and increase patient safety [1].
Providers and organizations were incentivized to exhibit
meaningful use of certified electronic health record (EHR)
systems in order to achieve this objective [1]. The HITECH Act
was based on the “triple aim” of health care, which consistedof reducing costs, improving the experience of care, andimproving population health, and the HITECH Act contributed
to the importance of EHRs [2]. Physicians and hospitals that
adopted and used certified EHRs as described in federally
defined “meaningful use” criteria were awarded approximately
US $27 billion in incentives [3] for eligible providers.
EHR adoption is an expensive investment, given the resources
and capital that are invested [4,5]. Due to the cost of theinvestment, a return on the EHR adoption investment is
expected. Usually, a return on adoption is measured by
calculating net profit and dividing the net profit by net
investment [6]. Calculating a return on investment for EHR
adoption requires considering the size of the organization, theextent of the EHR adoption, and profit or improvement in termsof both the financial and clinical outcomes perspectives. Given
the complex process of calculating return on investment for
EHR adoption, this study evaluates return on investment interms of how it yields value to the adopting entity. Value fromthe health care perspective has been described in terms of dollars(financial), productivity (clinical), effectiveness (clinical) [7],cost savings (financial) [8], improvement in clinical decisions(clinical; Rudin et al [9]), supporting triage decisions (clinical;
Rudin et al [9]), supporting collaborations among the providers(clinical; Rudin et al [9]), increased productivity (financial andclinical) [9], etc. However, a gap exists in that the return on
investment is not analyzed in terms of financial and clinicaloutcomes combined. Additionally, current literature does notreview EHR adoption in terms of level of EHR adoption but
rather as a binary variable of “adopted” or “not adopted.” This
study addresses these gaps by including a combination of bothfinancial and clinical outcomes in a conceptual model andreviewing EHR adoption in terms of levels of EHR adoption.
The value of health IT, of which EHRs are a subset, can dependon the stakeholder and context [10-12]. Looking at value fromthe stakeholder perspective, for the hospital, EHR value may
be reviewed in terms of improved revenue and reduced cost
(outcomes); for patients, value may be to improve health andhttps://medinform.jmir.org/2024/1/e52524

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prevent illness (outcomes); for providers, value may be to reduce
errors and provide efficient care (process and outcomes); andfor government, it may be to improve population health throughtimely public health reporting and population well-being(process and outcomes) [7]. Hence, given the frequent use ofdifferent outcome categories in the literature used to measurevalue, this study focuses on outcomes as the main value
construct and investigates value in terms of different tangible
outcomes, such as financial and clinical outcomes. This study
examined how EHR adoption levels are associated with value
in terms of financial and clinical outcomes combined in 1 model.
To address this question, this study investigated the relationshipbetween EHR adoption levels and financial and clinicaloutcomes by combining both financial and clinical outcomes
into 1 conceptual model.

Conceptual Framework and Hypotheses
This study used the corporate financial theory of the firm [13]to guide the evaluation of the relationship between EHR
adoption and financial and clinical outcomes. The corporate
financial theory of the firm (Figure 1) indicates that the valueof the firm, or in this case, the health care entity, is expected to
be in alignment with the discounted cash flows from theinvestments, such as EHRs [13]. This theory indicates that acapital investment, such as EHR adoption, increases the valueof the firm as it contributes toward an increase in the net present
value of cash flows [13]. Multiple studies have supported thenotion that EHR investments improve the value of a hospitalthrough improved financial outcomes by way of a reduction incost or improved revenues [4,14,15].
A study conducted by Collum et al [4] used this theory to
determine an association between EHR adoption and financial
outcomes (measured as profit margins and return on assets).
The findings from this study indicated that financial returns
depend on how long it takes for a hospital’s EHR system toachieve full functionality [4], meaning it is important to consider
the time variable when reviewing the outcomes of EHR
adoption.
Additionally, there have been several studies that have analyzed
the relationship between EHR adoption and financial outcomes
without using the corporate financial theory of the firm as theirguiding framework. Some of the studies from the current
literature exhibited a trend that EHR adoption and financial
outcomes have a nonlinear relationship [16,17], and some ofthe studies indicated that EHR adoption resulted in improved
financial outcomes for health care organizations that adopted
it over time [14,18].

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Modi et al

Figure 1. The corporate financial theory of the firm. EHR: electronic health record.

The literature suggests that improvement in costs and revenues
is the result of improved clinical outcomes such as reductionof redundant tests [19], reduction of medication and hospital
bed-related costs [20], improved ability to capture charges [15],and improved decision support systems [21]. Since this study
focuses on combining both financial and clinical outcomes into
1 conceptual model, for the purpose of this study, the capitalproject investment (EHR adoption in this case) and improvement
in financial returns (financial outcomes), tenets of the corporate
financial theory of the firm, with an addition of the clinicaloutcomes, are integrated into a conceptual framework.

The purpose of this conceptual framework (Figure 2 [4,22-27])
is to determine if the previously stated overarching researchquestion of “How is electronic health record adoption associatedwith value in terms of financial and clinical outcomes?” issupported by the following hypothesis: “The relationshipbetween levels of EHR adoption and financial outcomes (bothoperating margin and total margin) is moderated by clinicaloutcomes (readmission rate and length of stay [LOS]) that arealso affected by levels of EHR adoption (Figure 2).

Figure 2. Electronic health record (EHR) value analysis conceptual framework.

Methods
Data for this study were retrieved from multiple sources,
including the Health Care Provider Cost Reporting Information
System, the American Hospital Association (AHA) Annual
Survey, the AHA IT Supplement Survey, and Health Care
Analytics from Leavitt Partners. The study used a longitudinal
design from 2014 to 2017 with 5897 hospital-year observations.
Measures were divided into 2 groups: financial and clinical.
Financial outcome variables were measured or operationalized
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using 2 variables (operating margin and total margin) that havebeen used in the health care literature to measure the profitabilityof hospitals after EHR adoption. The variables describing
clinical outcomes are LOS and readmission rates, as thesevariables have an impact on the financial performance of thehospital [28,29]. The variables describing the financial outcomesare operating margin and total margin, as these measures include
both costs and revenues described in the corporate financial
theory of the firm [4,13]. The dependent variables used in thisstudy (operating margins, total margins, LOS, and readmission
rates) are not comprehensive in terms of measuring financial
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JMIR MEDICAL INFORMATICSand clinical outcomes for a hospital; however, for the purposeof this study, these variables are considered sufficient, given
their potential association with one another.

Dependent Variables
Financial Outcome Variables
In order to gain an understanding of the financial performanceof acute care hospitals, profitability ratios are the mostfrequently used measures [30]; hence, this study included
operating margin and total margin as variables representing
financial outcomes. Operating margin captures the expenses
and revenues related to hospital operations. Total margin
measures or captures operating and nonoperating expenses andrevenues. The operating margin was calculated by dividing net
patient revenues less total operating expenses by net patientrevenues and multiplying the ratio by 100. The total margin
variable is calculated by dividing net income by total patientrevenue. The financial outcome variables are retrieved from the
AHA Annual Survey (2014-2017).

Clinical Outcome Variables
Clinical outcomes were measured using LOS and readmission
rates. Daniel et al [22] and Schreiber and Shaha [31] reported
an intersection of financial and clinical outcomes as a result of
EHR adoption and focused on LOS. These studies reported an
improvement in LOS due to EHR adoption, resulting in lower
plan premiums for patients [22] and costs [31]. Readmission
rates are a part of the value-based purchasing program, anddepending on the readmission rate, hospitals are penalized ona yearly basis, hence impacting hospital costs [28]. The
readmission rates were measured for 6 conditions or procedures,
as patients with these conditions are more likely to be readmitted
to the hospital. These conditions are: acute myocardial
infarction, chronic obstructive pulmonary disease, heart failure,pneumonia, coronary artery bypass graft surgery, and elective
primary total hip arthroplasty and total knee arthroplasty [32].
LOS captures the number of days a patient spent in the hospital.
Readmission rates indicate whether patients are readmitted tothe hospital within 30 days of being discharged. The average
LOS and readmission rates can be considered to be indicatorsof clinical quality outcomes by way of clinical quality measures[28]. Ben-Assuli et al [33] and Lee et al [34] have indicatedimprovements in average LOS and readmission rates as resultsof EHR adoption. To confirm these findings for the most recent
data, this study analyzes how EHR adoption influences bothaverage (LOS) and readmission rates for the selected sample.
The LOS variable is measured as the average number of daysa patient stays in one hospital. The readmission rate variable ismeasured as the readmission rate payment adjustment factor.
The full-year payment adjustment factor is based on data fromthe fiscal year Hospital Readmissions Reduction Program
performance period (ie, July 1, 2014, to June 30, 2017). The
minimum payment adjustment factor is 0.97 (ie, 3% maximum
penalty). The maximum payment adjustment factor is 1 (ie, nopenalty). Hospitals with higher payment adjustment factors havelower penalties [32].

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Modi et al

Independent Variables
The level of EHR adoption is considered the major explanatory
variable in this study. Hospitals are required to report the extent
of adoption of each of the 28 EHR functions to the AHA IT
Supplement Survey. The 28 EHR functions can be characterized
into 5 different categories: clinical documentation, results
viewing, computerized order entry, decision support, and bar
coding. Hospitals indicate if each function is implemented inall units, 1 unit, or is in some stage of planning. A study
conducted by Everson et al [23] emphasizes the reliability andvalidity of measuring hospital adoption of EHR with these 28
items.
In order to look at the extent of EHR adoption, Adler-Milstein
et al [24] created a continuous EHR adoption measure for eachhospital in each year in which they responded to the AHA IT
Supplement Survey. The continuous measure was constructed
as follows: for each function that was implemented in all units,a hospital received 2 points, and for each function that was
implemented in at least 1 unit, a hospital received 1 point.
According to the calculations, the total possible EHR adoption
score ranged from 0 to 56. In order to improve interpretability,
the measure was scaled by dividing each hospital’s total scoreby 56, which yielded an EHR score ranging from 0 to 1. This
strategy will be replicated in this study and applied to the EHR
adoption level [24].

Control Variables
Control variables for this study include time-variant variables
such as competition and payer mix. Control variables are
identified based on elements that may influence the level of
EHR adoption or hospital financial and clinical outcomes [4].
Since this study uses panel data, which accounts for changes infinancial outcomes within hospitals due to changes in levels of
EHR adoption, it is not essential to control for time-invariant
hospital characteristics such as size of the hospital, ownership,
system affiliation, and teaching status. For the purpose of thisstudy, time-variant components that may change over the years,
such as competition and payer mix, are considered control
variables [4].
The competition construct was operationalized using the
Herfindahl-Hirschman Index (HHI), which measures theconcentration of an industry in a designated market. HHI was
measured in terms of discharges for the health service area.
Payer mix was measured using the proportion of inpatient days
that were related to Medicare and Medicaid patients (Medicare
percentage = total facility Medicare days/total inpatient days,
and Medicaid percentage = total facility Medicaid days/total
inpatient days). The AHA Annual Survey was used to collectthe HHI and payer mix data.

Analysis
The unit of analysis for this study is at the hospital level. To
demonstrate the appropriateness of the variables, univariate
statistics and bivariate analyses were conducted. Bivariate
statistics were generated for both independent and dependent
variables of interest. Pairwise correlation analysis was conductedat the significance level of P<.05 in order to examine pairwise
correlation coefficients between the continuous variables.
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Multivariate relationships between different levels of EHR
adoption and financial and clinical outcomes, along with thetime-variant control variables, were examined using moderation
analysis with a longitudinal fixed effects model [35]. Since it
is unknown as to when hospitals begin experiencing
improvements in financial outcomes, additional analysis was
conducted using a 1- or 2-year lag for profit margin ratios [4].
Statistical significance was noted at the significance levels of
P<.10, P<.05, and P<.01, and all statistical analyses wereconducted in Stata (version 16; StataCorp).

Longitudinal Fixed Effects Moderation Analysis Model
A longitudinal fixed effects model with moderation analysis
was used to analyze the multivariate relationships between
different levels of EHR adoption and financial and clinicaloutcomes, along with the time variant control variables.

Modi et al
yit = β1Xit1 + β2Xit2 + β3 Xit1 Xit2 + Zitλ + αi + μit
In this equation, yit is the dependent variable (financial or
clinical outcomes), i = hospital, and t = time. β1 is the coefficientfor the main independent variable (levels of EHR adoption),
Xit1. β2 is the coefficient for the moderator variable (clinicaloutcomes), Xit2. β3 is the coefficient for the interaction of the
independent variable (levels of EHR adoption) and moderator
variable (clinical outcomes), Xit1Xit2. Zitλ represents all control
variables (competition, payer mix, and years of observation).
αi is the unknown intercept for a vector of hospitals. And μit isthe error term.
The hypothesis, that the relationship between EHR adoption
and financial outcomes is moderated by clinical outcomes, wastested using multiple models. The models and their use are
outlined in Textbox 1.

Textbox 1. Analytic models and their use.
Model 1
Determine the association between levels of electronic health record (EHR) adoption and operating margin moderated by length of stay (LOS) withthe operating margins from the same year.
Model 2
Determine the association between levels of EHR adoption and operating margin moderated by readmission rates with the operating margins fromthe same year.
Model 3
Determine the association between levels of EHR adoption and total margin moderated by LOS with the total margins from the same year.
Model 4
Determine the association between levels of EHR adoption and total margin moderated by readmission rates with the total margins from the same
year.
Model 5Determine the association between levels of EHR adoption and operating margin moderated by LOS with a 1-year lag in the operating margins.
Model 6
Determine the association between levels of EHR adoption and operating margin moderated by LOS with a 2-year lag in the operating margins.
Model 7
Determine the association between levels of EHR adoption and operating margin moderated by readmission rates with a 1-year lag in the operating
margins.
Model 8Determine the association between levels of EHR adoption and operating margin moderated by readmission rates with a 2-year lag in the operating
margins.
Model 9Determine the association between levels of EHR adoption and total margin moderated by LOS with a 1-year lag in the total margins.
Model 10Determine the association between levels of EHR adoption and total margin moderated by LOS with a 2-year lag in the total margins.
Model 11
Determine the association between levels of EHR adoption and total margin moderated by readmission rates with a 1-year lag in the total margins.
Model 12
Determine the association between levels of EHR adoption and total margin moderated by readmission rates with a 2-year lag in the total margins.

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Ethical Considerations
This study was approved by the University of Alabama at
Birmingham institutional review board (300003241).

Results
Overview
Descriptive statistics of acute care hospitals for the years
2014-2017 are displayed in Table 1. For acute care hospitals,
average EHR adoption levels showed little variability acrosseach observed year (approximately 0.89 for each observed year).
Hospitals observed a steady decrease in average operating
margin from 2014 (0.07%) to 2017 (0.057%). The average total
margin across hospitals showed a decrease for 2015 (0.005%)
compared with 2014 (1.014%), followed by a steady increase
across years 2016 (1.136%) and 2017 (0.951%). An increasein LOS was observed for the years 2016 and 2017
(approximately 7.9 days for the year 2017 vs 3.9 days for theyear 2014). Average readmission rates remained somewhat
steady across all 4 observation years (approximately 0.99 foreach observed year).

Table 1. Descriptive statistics of variables (N=5678 hospital-year observations).
Variables

2014 (n=1420)

2015 (n=1453)

2016 (n=1393)

2017 (n=1412)

Levels of EHRa adoption, mean (SD)0.871 (0.127)

0.890 (0.121)

0.899 (0.127)

0.917 (0.102)

Operating margin, mean (SD)0.070 (0.122)

0.065 (0.132)

0.06 (0.140)

0.057 (0.136)

Total margin, mean (SD)1.014 (2.847)

0.005 (26.4)

1.136 (7.217)

0.951 (1.129)

Average length of stay (days), mean (SD)

3.911 (1.134)

3.881 (0.954)

7.87 (153.3)

7.945 (160.4)

Readmission rate, mean (SD)

0.998 (0.003)

0.995 (0.006)

0.995 (0.006)

0.994 (0.007)

Market competition (HHIb) in terms of discharges, mean(SD)

0.101 (0.199)

0.086 (0.157)

0.088 (0.172)

0.098 (0.193)

Medicare percentage, mean (SD)0.512 (0.140)

0.518 (0.128)

0.518 (0.130)

0.521 (0.124)

Medicaid percentage, mean (SD)0.197 (0.120)

0.202 (0.115)

0.203 (0.114)

0.204 (0.112)

Beds (n), mean (SD)

257 (231)

256 (229)

254 (232)

255 (236)

Nongovernment not-for-profit

1105 (77.76 )1145 (78.8 )1177 (78.31)

1198 (78.82)

Investor-owned for-profit

294 (20.69)

295 (20.30)

311 (20.69)

305 (20.07)

Government nonfederal

22 (1.55)

13 (0.89)

15 (1)

17 (1.12)

Yes

584 (47.29)

660 (51.36)

687 (51.58)

731 (56.67)

No

651 (52.71)

625 (48.64)

645 (48.42)

559 (43.33)

Yes

560 (39.41)

569 (39.16)

595 (39.59)

599 (39.41)

No

861 (60.59)

884 (60.84)

908 (60.41)

921 (60.59)

Ownership, n (%)

Affiliation, n (%)

Teaching status, n (%)

a

EHR: electronic health record.

b

HHI: Herfindahl-Hirschman Index.

For time-variant control variables, the average HHI in terms ofdischarges across all 4 years was approximately 0.093. HHIvalues range from 0 to 1, where an HHI value closer to 1 means
monopolistic markets, or more market share, and an HHI value
closer to 0 means highly competitive markets, or less market
share. For the sample used in this study, the markets appear tobe highly competitive. In terms of payer mix, the Medicare
percentage was similar across all 4 years (average of 0.52).
Similarly, the Medicaid percentage was also similar across all
4 years (average of 0.20).
For organizational characteristics, bed size was somewhat
similar across all hospitals for all observed years (approximately
255 beds per hospital). In terms of ownership status of thesample hospitals, a majority of the hospitals werehttps://medinform.jmir.org/2024/1/e52524

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nongovernment, not-for-profit hospitals (1105/1421, 78%),
followed by investor-owned for-profit hospitals (294/1421,
20%) and government nonfederal hospitals (22/1421, 1.5%).
In terms of system affiliation, approximately half the hospitals
were affiliated with a system, and the other half were not. For
teaching status, a majority of the hospitals did not hold ateaching status (861/1421, 61%).
According to the bivariate statistical analysis (Table 2), at thesignificance level of P<.05, levels of EHR adoption exhibit apositive correlation with operating margin at a magnitude of
0.0978. At the significance level of P<.05, readmission rate andlevels of EHR adoption are negatively correlated at themagnitude of 0.0321. Even though the magnitudes are close to

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Modi et al0, these relationships are statistically significant at thesignificance level of P<.05.

Table 2. Bivariate analysis of variables.

aDependent variables

Independent variables: levels of EHRa adoption (correlation coefficients)

Operating margin

0.0978b

Total margin–0.0142

Average length of stay0.0039Readmission rate

–0.0321b

EHR: electronic health record.

bP<.05.

This study tested the following hypothesis that was derived
from the EHR value analysis conceptual framework (Figure 2):“The relationship between EHR adoption and financial outcomesis moderated by clinical outcomes.” Tables 3 and 4 provide
details relative to the hypothesis.

Table 3. Fixed effects with regression analysis.
Variables

Model 1Model 2OMa–LOSb–levels of EHRc OM–RRd–levels of EHR
adoption (Prob>F=0.0828) adoption (Prob>F=0.0116)

Model 3Model 4

TMe–LOS–levels of EHR
adoption (Prob>F=0.4532)

TM–RR–levels of EHR
adoption (Prob>F=0.3388)–0.0205.335f–4.961

415.2

Average LOS

0.000

N/Ag

–0.002

N/A

RR

N/A

4.375hN/A

431.6

Levels of EHR adoption
and average LOS-0.000

N/A

0.001

N/A

Levels of EHR adoption
and RR

N/A–5.384f

N/A–422.3

0.082

0.078

3.148

2.959

Medicare percentage–0.009–0.013

0.699

0.937

Medicaid percentage–0.026–0.026

1.211

1.343

Reference

Reference

Levels of EHR adoption
Dependent variables

Control variables
Market competition(HHIi)

Years
2014

a

Reference

Referencef

2015–0.005–0.007–1.001–0.8482016–0.008–0.009f

0.388

0.569

2017

–0.008–0.011j

0.243

0.460

OM: operating margin.

b

LOS: length of stay.

c

EHR: electronic health record.

d

RR: readmission rate.

e

TM: total margin.

f

P<.05.

g

N/A: not applicable.

hP<.10.

i

HHI: Herfindahl-Hirschman Index.

jP<.001.

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Table 4. Regression analysis with fixed effects for lagged variables.
Variables

Model 5Model 6

Model 7

Model 8Model 9Model 10Model 11

Model 12OMa–LOSb–lev- OM–LOS–lev- OM–RRd–levels of EHR
els of EHR
els of EHRc
adoption with adoption withadoption with
2-year lag
1-year lag
1-year lag(Prob>F=0.0271) (Prob>F=0.0010)(Prob>F=0.0047)

OM–RR–levels of EHR
adoption with
2-year lag(Prob>F=0.0202)

TMe–LOS–levels of EHR
adoption with
1-year lag(Prob>F=0.6885)

TM–LOS–levels of EHR
adoption with
2-year lag(Prob>F=0.6738)

TM–RR–levels of EHR
adoption with
1-year lag(Prob>F=0.6492)

TM–RR– levels of EHR
adoption with
2-year lag(Prob>F=0.5143)

Levels of EHR
adoption

0.022

0.004

1.681

2.229

1.564

–1.547–164.0

268.8

Average LOS

0.000–9.46e–06

N/Af

N/A

0.001

–0.012

N/A

N/A

RR

N/A

N/A

0.818

2.192

N/A

N/A

–186.4169.8

Levels of EHR
adoption andaverage LOS–0.000–4.26e–06

N/A

N/A

–0.002

0.013

N/A

N/A

Levels of EHR
adoption and
RR

N/A

N/A

–1.663–2.232

N/A

N/A

166.2

–271.7

Market competi- 0.219htion (HHIg)–0.0680.223h–0.0683.610–3.2753.749–3.103

Medicare percentage

0.063i

0.024

0.068h0.030–2.4190.523–2.416

0.783

Medicaid percentage

0.018

0.891h

0.018

0.092h1.742–2.8221.741–2.8382014

Reference

Reference

Reference

Reference

Reference

Reference

Reference

Reference

2015

0.014h–0.0060.014h–0.006–1.057–0.714–1.178–0.9102016

0.011h–0.008i

0.009–0.008i

0.141

0.482

0.048

0.323

2017

0.010h–0.018j

0.009i–0.018h

0.008

0.508

–0.126

0.229

Years

a

OM: operating margin.

b

LOS: length of stay.

c

EHR: electronic health record.

d

RR: readmission rate.

e

TM: total margin.

f

N/A: not applicable.

g

HHI: Herfindahl-Hirschman Index.

hP<.05.

i

P<.10.

jP<.001.

EHR: Length of Stay (Operating Margin and Total
Margin)Model 1 analyzed the relationship between EHR adoption levels
and operating margins without any lags in operating margins,with LOS as a moderating variable for acute care hospitals. For
Model 1, the prob>F was greater than 0.05, meaning this model
did not provide a statistical explanation for the proposed
relationship between EHR adoption levels and operating margins
with LOS as a moderating variable.
Model 5 analyzed the relationship between EHR adoption levels
and operating margins with a 1-year lag in operating margins,with LOS as the moderating variable for acute care hospitals.
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The prob>F was less than .05 for this model; however, theanalysis did not provide statistically significant evidence tosupport the relationship between EHR adoption levels andoperating margins with a 1-year lag in operating margins, with
LOS as a moderating variable. The nonsignificant results
indicated a direct positive association between EHR adoption
levels and operating margins and LOS; however, when LOS
acts as a moderating variable, the indirect relationship between
EHR adoption levels and operating margins was negative.
Model 6 analyzed the relationship between EHR adoption levels
and operating margins with a 2-year lag in operating margins,with LOS as a moderating variable for acute care hospitals.
Even though the prob>F was less than .05 for this model, the
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JMIR MEDICAL INFORMATICS
analysis did not provide statistically significant evidence tosupport the relationship between EHR adoption levels andoperating margins with a 1-year lag in operating margins, with
LOS as a moderating variable. The nonsignificant results
indicated a direct positive association between EHR adoption
levels and operating margins with a 2-year lag, which was
expected. Additionally, the nonsignificant results indicated a
direct negative association between EHR adoption levels and
LOS, which is consistent with the findings from the literature.
However, when LOS is introduced as a moderating variable,
the nonsignificant results indicate a negative indirect relationshipbetween EHR adoption levels and operating margins with a
2-year lag.
Model 3 analyzed the relationship between EHR adoption levels
and total margins without any lags in total margins, with LOS
as a moderating variable for acute care hospitals. The prob>Fwas greater than 0.05, meaning the models did not provide astatistically significant explanation for the proposed relationshipbetween EHR adoption levels and total margins without any
lags in total margins, with LOS as a moderating variable.
Model 9 analyzed the relationship between EHR adoption levels
and total margins with a 1-year lag in total margins, with LOS
as a moderating variable for acute care hospitals. For Model 9,
the prob>F was greater than 0.05, meaning this model could
not accurately predict the relationship between EHR adoption
levels and total margins with a 1-year lag in total margins, with
LOS as a moderating variable.
Model 10 analyzed the relationship between EHR adoption
levels and total margins with a 2-year lag in total margins, with
LOS as a moderating variable for acute care hospitals. For
Model 10, the prob>F was greater than 0.05, which indicatesthat this model could not accurately predict the relationshipbetween EHR adoption levels and total margins with a 2-year
lag in total margins, with LOS as a moderating variable.

EHR: Readmission Rate (Operating Margin and Total
Margin)Model 2 analyzed the relationship between EHR adoption levels
and operating margins without any lags in operating margins,with readmission rates as a moderating variable for acute care
hospitals. Hospitals with higher readmission payment adjustment
factors have lower penalties [32]. This was the only model inwhich the results from the analysis provided statisticallysignificant evidence to support the proposed relationship. At
the significance level of P<.05, EHR adoption levels werepositively associated with operating margins. Similarly, at thesignificance level of P<.05, readmission rates were positively
associated with an increase in operating margin. However, whenreadmission rates are introduced as a moderating variable, themagnitude of the relationship between levels of EHR adoption
and operating margins is negative.
Model 7 analyzed the relationship between EHR adoption levels
and operating margins with a 1-year lag in operating margins,with readmission rates as a moderating variable for acute care
hospitals. For Model 7, the prob>F was less than .05 for thismodel; however, the analysis did not provide statisticallysignificant evidence to support the relationship between EHR
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Modi et al
adoption levels and operating margins with a 1-year lag inoperating margins, with readmission rates as a moderating
variable. The nonsignificant results indicate a direct positive
association between EHR adoption levels and operating margins
with a 1-year lag and readmission rates, which is consistent
with the findings from Model 2. However, when readmission
rates act as a moderating variable, the nonsignificant results
indicate a positive relationship between levels of EHR adoption
and operating margins with a 1-year lag, which was the oppositeof the results from Model 2.
Model 8 analyzed the relationship between EHR adoption levels
and operating margins with a 2-year lag in operating margins,with readmission rates as a moderating variable for acute care
hospitals. The prob>F was less than .05 for this model; however,
the analysis did not provide statistically significant evidence tosupport the relationship between EHR adoption levels andoperating margins with a 2-year lag in operating margins, withreadmission rates as a moderating variable. Similar to Model7, the nonsignificant results indicated a direct positive
association between EHR adoption levels and operating margins
with a 2-year lag and readmission rates, which was consistent
with the findings from Model 2. However, when readmission
rates act as a moderating variable, the nonsignificant results
indicated a positive relationship between levels of EHR adoption
and operating margins with a 2-year lag, which was the oppositeof the results from Model 2.
Model 4 analyzed the relationship between EHR adoption levels
and total margins without any lags in total margins, withreadmission rates as a moderating variable for acute care
hospitals. The prob>F was greater than 0.05, meaning this model
could not provide a statistically significant explanation for the
proposed relationship between EHR adoption levels and total
margins without any lags in total margins, with readmission
rates as a moderating variable.
Model 11 analyzed the relationship between EHR adoption
levels and total margins with a 1-year lag in total margins, withreadmission rates as a moderating variable for acute care
hospitals. For Model 11, the prob>F was greater than 0.05,
which indicates that this model could not accurately predict therelationship between EHR adoption levels and total margins
with a 1-year lag in total margins, with readmission rates as amoderating variable.
Model 12 analyzed the relationship between EHR adoption
levels and total margins with a 2-year lag in total margins, withreadmission rates as a moderating variable for acute care
hospitals. The prob>F was greater than 0.05, meaning this model
could not provide a statistically significant explanation for the
proposed relationship between EHR adoption levels and total
margins with a 2-year lag in total margins, with readmission
rates as a moderating variable.
Results from the regression analysis with fixed effects are
displayed in Tables 3 and 4. Table 3 includes results from theregression analysis with financial and clinical outcomes fromthe same year. Hospitals receive their reimbursement andpenalties associated with readmission rates and LOS
approximately 1 to 2 years after the actual outcomes occur. In
order to accommodate this situation, operating margin and total
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JMIR MEDICAL INFORMATICS
margin ratios were calculated with a 1- and 2-year lag. Table 4
presents results with lags in profit margins for acute care
hospitals.
The results from Table 3 for model 2 suggest that, at thesignificance level of P<.05, a 1-unit increase in EHR adoption
was associated with an increase of approximately 5.34% in theoperating margin.
Table 4 displays results from the analyses with the added lag
effect in operating and total margins. According to the results
displayed in Table 4, it can be inferred that at the significancelevels of P<.05, P<.10, or P<.001, there is not enough evidence
to support models 5-8 from this study. For models 9-12, themodels did not provide a statistical explanation for the proposed
relationships. In other words, the models discussed above could
not accurately predict the proposed relationships.

Discussion
Overview
The objective of this study was to determine how EHR adoption
level contributes to financial and clinical outcomes for acutecare hospitals.
To understand the relationship between EHR adoption leveland financial outcomes, moderated by clinical outcomes, thisstudy used a fixed effects moderation analysis model. We
hypothesized that there would be a positive association between
EHR adoption level and operating and total margins, with LOS
and readmission rates as moderating variables.
According to the results displayed in Table 3, for models 1, 3,
and 4, the prob>F was greater than .05, meaning the models did
not provide a statistical explanation for the proposed
relationships in these models. In other words, the models
discussed above could not accurately predict the proposed
relationships, and there is no evidence that EHR adoption levels
have a linear relationship with or explain variance in theoperating margins, total margins, and LOS.
Even though the results are inverse of what was predicted in
the hypothesis, these findings indicated that the relationshipbetween EHR adoption levels and operating margins was

Modi et al
statistically significant when it was moderated by thereadmission rates variable at the significance level of P<.05.
According to the results of the moderation analysis, as thereadmission rate increases by 1 unit, the effect of a 1-unit
increase in EHR adoption level on the operating margindecreases by 5.38%. In other words, when the hospital incurred
lower penalties for readmissions, the operating margins
increased. The minimum payment adjustment factor is 0.97 (ie,
3% maximum penalty). The maximum payment adjustment
factor is 1 (ie, no penalty), and hospitals with higher payment
adjustment factors have lower penalties and, in turn, larger
operating margins [32].
In order to confirm any lagged effect (the timeline of hospitals
receiving penalties or incentives for EHR adoption being notclear), this study included additional models that accounted for1- and 2-year lag in the profit margin ratios (models 5-12). The
results, however, did not provide any statistically significant
evidence supporting a positive relationship between EHR
adoption level and profit margin ratios when the lag effect was
included in the model.
Findings from current literature indicate an improvement in
LOS as a result of EHR adoption (not necessarily adoption
level) yielding increased compensation for the loss of patientdays from Center for Medicare and Medicaid Services [25];however, for this study, none of the tested models provided astatistical explanation for the proposed relationships between
EHR adoption and profit margins with LOS as moderating
variables.
Even though this finding is opposite of what was proposed in
the hypothesis, this finding provides statistically significant
evidence that levels of EHR adoption change operating margins
when readmission rates are taken into account (Figure 3).
Analyzing more recent data could indicate a decrease inreadmission rates as a result of increased levels of EHR
adoption, yielding an increase in operating margins. The
relationship between EHR adoption level and operating margins
has not been previously evaluated using readmission rates asmoderating variables. Hence, this finding from this study is aunique contribution to the current literature.

Figure 3. Electronic health record (EHR) value analysis framework with results. **P<.05.

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JMIR MEDICAL INFORMATICS

Limitations of This Study
Regardless of the valuable contribution of the buildout of theconceptual model and the results from the analysis, this study
has limitations. First, there is always a risk when using
secondary data to conduct research that was not the intent when
the data were collected, as this could result in inconsistency inthe data collection methods due to the possibility of human error[36].
Second, this study used data from the Medicare Cost Reports
to operationalize the readmission rate variable. This particularmeasure is reported on a 3-year rolling basis, meaning the data
analyzed included a rolling average of 3 years of readmission
rate data for each hospital [32]. This study operationalized thereadmission rate data for specific years in order to evaluate theirrelationship with levels of EHR adoption and financial
outcomes, which can be considered a limitation.

Conclusion
The HITECH Act has played an important role in EHRs
becoming an integral part of the modern health system over thelast 10 years. The goal of enacting the HITECH Act of 2009
was to reduce health care costs, improve the quality of the careprovided, and increase patient safety for providers andModi et alorganizations that exhibited meaningful use of certified EHR
systems [1,37]. Given the cost and complexity of EHR adoption,
analyzing its value from various and seemingly atypicalperspectives is essential.
The current literature does a good job of providing perspectives
on EHR value relative to individual financial and clinicaloutcomes, but it falls short in providing a collective value
analysis. This study fills the gap in the literature by evaluating
individual relationships between EHR adoption levels andfinancial and clinical outcomes, in addition to evaluating therelationship between EHR adoption level and financial
outcomes, with clinical outcomes as moderators.
This study provided statistically significant evidence, indicatingthat there is a relationship between EHR adoption level andoperating margins when this relationship is moderated byreadmission rates. This finding could further be supported byevaluating more recent data to analyze whether hospitals
increasing their level of EHR adoption would decrease
readmission rates, resulting in an increase in operating margins.
Hospitals would incur lower penalties as a result of improved
readmission rates, which would contribute toward improved
operating margins.

Conflicts of Interest
Not applicable.

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Abbreviations
AHA: American Hospital Association
EHR: electronic health record
HHI: Herfindahl-Hirschman Index
HITECH: Health Information Technology for Economic and Clinical Health
LOS: length of stay

Edited by J Hefner; submitted 06.09.23; peer-reviewed by L Heryawan, A Kotlo; comments to author 23.10.23; revised version received
29.10.23; accepted 29.11.23; published 24.01.24
Please cite as:
Modi S, Feldman SS, Berner ES, Schooley B, Johnston A
Value of Electronic Health Records Measured Using Financial and Clinical Outcomes: Quantitative StudyJMIR Med Inform 2024;12:e52524
URL: https://medinform.jmir.org/2024/1/e52524
doi: 10.2196/52524
PMID: 38265848©Shikha Modi, Sue S Feldman, Eta S Berner, Benjamin Schooley, Allen Johnston. Originally published in JMIR Medical
Informatics (https://medinform.jmir.org), 24.01.2024. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The
complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyrightand license information must be included.

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