Table 2.
Reviewed studies on the impact of EHRa adoption and financial and clinical outcomes.
| Study | Journal or conference | Study period or data set | Objective | Outcome measures | Financial (n=21) | Clinical (n=54) |
| Adler-Milstein et al [18] | Health Services Research | AHA IT Supplement Survey (2008-2011), AHA Annual Survey (2009-2012), CMSb Hospital Compare data set (2009-2012), and CMS EHR Incentive Program Reports | To examine the relationship between EHR adoption and hospital outcomes | Efficiency (measured by the ratio of a hospital’s total expenditures to adjusted patient days), process adherence, and patient satisfaction | ✓c | ✓ |
| Appari et al [31] | The American Journal of Managed Care | Cross-sectional retrospective study, data on hospital patient safety performance (2008-2010) combined with IT systems data (2007; n=3002 nonfederal acute care hospitals) | To determine whether HITd systems are associated with better patient safety in acute care settings | Adverse event indicators developed by AHRQe (death among surgical patients with serious, treatable complications; collapsed lung that results from medical treatment [iatrogenic pneumothorax]; breathing failure after surgery [postoperative respiratory failure]; blood clots in the lung or a large vein after surgery [postoperative pulmonary embolism or deep venous thrombosis]; wounds that split open after surgery [postoperative wound dehiscence]; accidental cuts and tears [accidental puncture or laceration]; death after surgery to repair a weakness in the abdominal aorta [abdominal aortic aneurysm mortality rate]; and death among patients with hip fractures [hip fracture mortality rate]) |
|
✓ |
| Bae et al [32] | BioMed Central Health Services Research | National Ambulatory Medical Care Survey (37,962 patient visits to 1470 primary care physicians from 2006 to 2009) | To analyze the impact of EHRs on primary care physicians’ workloads | Duration measured in minutes of the face-to-face encounter between physicians and patients (patient face time) for direct patient care during the office visit and number of total patient office visits per physician per week (patient volume) |
|
✓ |
| Behkami et al [33] | Studies in Health Technology and Informatics | Simulation of clinic-type scenarios to capture the dynamic nature of policy interventions that affect the adoption of EHR | To describe a framework that allows decision-makers to efficiently evaluate factors that affect EHR adoption and test financial incentives | Revenue | ✓ |
|
| Bishop et al [34] | Health Affairs | Interviews of medical group leaders (n=21) who use electronic communication with patients extensively and staff from 6 of the groups | To understand how primary care practices can use electronic communication to manage clinical issues that are usually managed during clinic visits; determine perceived advantages and disadvantages of the electronic communication programs for patients, physicians, and practices; and determine the barriers to and facilitators of the implementation of the electronic communication programs | The convenience of access, patient satisfaction, efficiency, safety and quality of care, and workload |
|
✓ |
| Brown Jr et al [29] | Journal of Addiction Medicine | Data collected from paper patient charts (for preimplementation data) and electronic patient charts (for postimplementation data); patients, clinicians, and management stakeholders participated in surveys | To evaluate the impact of an EMRf system on the Opioid Agonist Treatment Program | Financial performance (revenue), quality (timeliness of medical assessments), productivity (clinic visits), patient satisfaction, and risk management (incident reports) | ✓ | ✓ |
| Bucher et al [35] | Journal of the American College of Surgeons | CMS SCIPg measuring compliance rates; HIMSSh hospital EHR adoption survey from 2006 to 2012 | To analyze the impact of EHR adoption on hospital compliance with quality and process measures | Hospital compliance with SCIP core measures |
|
✓ |
| Burke et al [36] | Journal of Innovation in Health Informatics | Notes of outpatients with type 2 diabetes analyzed (n=537) for 5.5 years | To analyze the impact of EHR use on clinical quality measures and HbA1ci | HbA1c values |
|
✓ |
| Cheriff et al [37] | International Journal of Medical Informatics | The practice management system used to extract physician productivity data (n=203) | To describe the changes in physician productivity in an academic multispecialty group because of ambulatory EHR adoption | Average monthly charge, visit volume, and work-relative value units | ✓ | ✓ |
| Chiang et al [38] | Journal of American Association for Pediatric Ophthalmology and Strabismus | Academic pediatric ophthalmology practice data for the year 2006 (n=4 faculty providers) | To analyze the impact of EHR implementation on the volume and time for pediatric ophthalmology | Clinical volume |
|
✓ |
| Chiang et al [39] | Transactions of the American Ophthalmological Society | Outpatient clinical examinations (n=120,490) from faculty providers (n=23) at an academic ophthalmology department analyzed for 3 years | To evaluate clinical volume, time requirements, and nature of clinical documentation related to EHR implementation | Clinical volume, time requirements, and nature of clinical documentation |
|
✓ |
| Choi et al [40] | Journal of Medical Systems | Retrospective chart review study—a convenience sample of 60 to 80 charts reviewed every month from (January 1, 2006, to October 4, 2009, n=3997; October 5, 2009, to December 31, 2010, n=984) | To analyze the organizational performance and regulatory compliance before and after implementation of the Anesthesia Information Management System | Documentation of medication and patient status |
|
✓ |
| Collum et al [8] | Healthcare Management Review | AHAj Annual survey (2007-2010), AHA IT Supplement (2007-2010), and CMS Medicare Cost Reports (2007-2011) | To examine how EHR adoption affects hospital financial performance | Profit margins and return on assets | ✓ |
|
| Dandu et al [41] | Clinical Orthopedics and Related Research | Data were collected from a combination of the Physician Compare data set (2016), Meaningful Use Eligible Professional public use files (2011-2016), and Medicare Utilization and Payment data sets (2012-2016) | To evaluate the impact of EHRs on provider productivity, billing, and orthopedic surgery | Billing, outpatient volume, and surgical volume | ✓ | ✓ |
| Daniel et al [42] | Academic Emergency Medicine | Health plan and electronic hospital data from a large urban EDk (November 1, 2004, to March 31, 2005, n=1509 ED encounters compared with September 1, 2005, to February 17, 2006, n=779 ED encounters) | To evaluate the use of paper-based EHR in an ED on LOSl and plan payments | Plan payment for ED encounters and ED LOS | ✓ | ✓ |
| Deily et al [43] | Health Research and Educational Trust | Administrative claims data in Pennsylvania from 1998 to 2004 (n=491,832) | To examine whether HIT at nonhospital facilities improves health outcomes and decreases resource use at hospitals within the same network and whether the effect of HIT differs as providers obtain more experience with it | Incidence of obstetric trauma and preventable complications; LOS |
|
✓ |
| Edwardson et al [44] | Medical Care Research and Review | Financial panel data from the pediatric primary care network comprising 260 providers across 42 practices (2008-2013) | To examine the effect of EHR adoption on charge capture | Average per-patient charge, average per-patient collections, and charge-to-collection ratios | ✓ |
|
| Ehrlich et al [45] | Applied Clinical Informatics | Survey responses from 32 ophthalmologists after implementation, 28 at 3 months, 35 at 7 months, 40 at 13 months, and 39 at 24 months after implementation (implementation in 2012) | To comprehend and describe the perceptions of ophthalmologists during EHR implementation in an academic department of ophthalmology | Documentation quality, workflow, and efficiency |
|
✓ |
| Flatow et al [46] | Applied Clinical Informatics | Retrospective chart review for all patients admitted to the surgical intensive care unit (n=3742; January 1, 2009, to December 31, 2010) | To evaluate key quality measures of a surgical intensive care unit following EHR implementation in a tertiary hospital | LOS, mortality, central line–associated bloodstream infection rates, clostridium difficile colitis rates, readmission rates, and number of coded diagnoses |
|
✓ |
| Furukawa et al [47] | Journal of the American Medical Informatics Association | Data collected from Medicare Patient Safety Monitoring System (2010-2013) and HIMSS Analytics database (2008-2013) | To evaluate the impact of meaningful use capabilities on in-hospital adverse drug events | Rate of adverse drug events |
|
✓ |
| Han et al [48] | American Journal of the Medical Sciences | A prospective observational study (n=797 patients) at an urban teaching hospital from July 2010 to June 2011 in the MICUm | To determine the effect of EHR on MICU mortality, hospital LOS, and medication errors | MICU mortality, hospital LOS, and medication errors |
|
✓ |
| Hepp et al [17] | Value in Health | The decision-analytic model was used to estimate the cost-effectiveness of CPOEn in a multidisciplinary medical group for the years 2010 to 2014 (n=400 providers) | To assess the cost-effectiveness of CPOE in the reduction of medication errors and adverse drug events in an ambulatory setting | Costs (CPOE system costs, personnel costs, administrative costs, and prescribing costs), financial incentives (Health Information Technology for Economic and Clinical Health meaningful use incentives and pay-for-performance incentives), medication error probability, and adverse drug event probability | ✓ | ✓ |
| Herasevich et al [49] | Critical Care Medicine | A prospective study at Mayo Clinic, Rochester, Minnesota (n=1159 patients) from February 16, 2008, to February 16, 2009 | To design and test an electronic algorithm that includes patient characteristics and ventilator settings, allowing notification to bedside providers about potentially injurious ventilator settings to improve the safety of ventilator care and decrease the risk of ventilator-related lung injury | Prevalence of acute lung injury |
|
✓ |
| Hessels et al [50] | Online Journal of Nursing Informatics | Data on 854,258 adult patients discharged from 70 New Jersey hospitals and 7679 nurses working in those same hospitals for the year 2006 | To examine the relationship between the EHR adoption stage, missed nursing care, nursing practice environment, and adverse outcomes and satisfaction of patients who are hospitalized | Prolonged LOS and patient satisfaction |
|
✓ |
| Howley et al [51] | Journal of the American Medical Informatics Association | Compared practice productivity and reimbursement of ambulatory practices (n=30) for 2 years after EHR implementation to their per-EHR implementation baseline | To evaluate how EHR implementation affects the financial performance of ambulatory practices | Reimbursement and practice productivity (number of patient visits) | ✓ | ✓ |
| Jones et al [52] | American Journal of Managed Care | Database with 2021 hospitals collected by linking the AHA Annual Survey database, Hospital Compare database, and HIMSS database for the years 2004 and 2007 | To analyze longitudinal data on EHR adoption to evaluate the impact of new EHR adoption on quality improvement | Composite measures of hospital process quality for acute myocardial infarction, health failure, and pneumonia |
|
✓ |
| Katzer et al [53] | Applied Clinical Informatics | Prehospital patient care reports (n=154) at Georgetown University’s student-run Emergency Medical Services organization | To describe whether implementing an electronic patient care report system influenced improvement in physical exam documentation | Mean physical exam documentation |
|
✓ |
| Kritz et al [54] | Journal of Evaluation in Clinical Practice | Opioid treatment program clinics (7 clinics) in New York State—paper patient charts and electronic patient charts (to analyze pre- and postimplementation data), assessment meetings and surveys with patients, direct care providers, and supervisors or managers | Prospective, comparative study using a pre- and postimplementation design to establish whether EHR implementation yielded any improvements | Revenue, quality, productivity, risk management, and satisfaction | ✓ | ✓ |
| Lam et al [55] | BioMed Central Health Services Research | Data from physicians with practices at the University of Washington Department of Ophthalmology for the years 2008 to 2012 (n=8 physicians) | To analyze the impact of EHR adoption on patient visit volume at an academic ophthalmology department | Patient volume per provider |
|
✓ |
| Lim et al [28] | Journal of American Medical Association Ophthalmology | Population-based, cross-sectional study (n=348) | To evaluate the adoption rate and perceptions of financial and clinical outcomes of EHRs among ophthalmologists in the United States | Net revenues and productivity | ✓ | ✓ |
| Love et al [3] | Journal of American Medical Informatics Association | 2007 state-wide survey of Massachusetts physicians (n=541) | To characterize and describe physicians’ attitudes toward EHR’s potential to cause new errors, improve health care quality, and change physician satisfaction | Medical errors, quality of care, and physician satisfaction |
|
✓ |
| Lowe et al [56] | Journal of Wound Ostomy Continence Nurses Society | Data were collected from a regional Veterans Affairs database and computerized patient medical records for a year after implementation of the EMR wound care template (October 1, 2006, to September 30, 2007) and 2 years before the intervention | To evaluate the impact of a 1-year intervention of an EMR wound care template on the completeness of wound care documentation and medical coding and compare results with the preintervention period | Documentation of wound care and documentation of coding for diagnoses and procedures |
|
✓ |
| McCullough et al [57] | Health Affairs | AHA Annual Survey, HIMSS Analytics, and CMS Hospital Compare database for the years 2004 to 2007 (n=3401 nonfederal acute care US hospitals) | To analyze the impact of HIT on the quality of care in US hospitals | Quality indicators: percentage of patients with heart failure given angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker for left ventricular systolic dysfunction; the percentage of smokers with heart failure and pneumonia who were given smoking cessation advice; the percentage of patients with pneumonia assessed and given pneumococcal vaccination if indicated; the percentage of patients with pneumonia whose initial blood culture in the ED preceded their first dose of the hospital-administered antibiotics; and the percentage of patients with pneumonia given the most appropriate initial antibiotic |
|
✓ |
| McCullough et al [58] | Generating Evidence and Methods to improve patient outcomes) | Manual review of the paper and electronic charts for 6007 patients across 35 small primary care practices | To analyze the quality measure performance in small practices before and after EHR adoption | Clinical quality measures: antithrombotic therapy, BMI recorded, smoking status recorded, smoking cessation intervention offered, HbA1c testing and control, cholesterol testing and control, and BPo control |
|
✓ |
| Mirani and Harpalani [27] | ACM Transactions on Management Information Systems | “Data and Reports” and “Hospital Cost Report” from the CMS website for 2008 to 2010 | To analyze the impact of the Medicare EHR incentive program on acute care hospitals | The average cost of ancillary services per patient, profit margins, inpatient bed debts, outpatient bed debts, and patient stay durations | ✓ | ✓ |
| Mitchell et al [59] | The Journal of Rural Health | AHA EHR adoption survey and CMS Hospital Compare data set for the year 2009 | To investigate whether there is an association between clinical decision support system use and quality disparities in pneumonia process indicators between rural and urban hospitals | Percentage of hospitals meeting quality requirements and pneumonia process composite scores |
|
✓ |
| Patterson et al [60] | Applied Clinical Informatics | Data used from the AHA Health IT survey and Medicare Part A claims (n=52,048 Medicare beneficiaries discharged for heart failure anytime during the calendar year 2008) | To compare 30 days all-cause readmission rates for Medicare patients with health failure discharged from hospitals with fully implemented comprehensive EHR vs without it | 30-day all-cause readmission rates |
|
✓ |
| Persell et al [61] | Medical Care | Time series analysis at a large internal medicine practice from February 1, 2007, to February 1, 2009 (n=12,299 patients eligible at the beginning of the intervention) | To implement and analyze a multifaceted quality improvement intervention using EHRs as tools for improving performance | Quality measures pertaining to coronary heart disease, health failure, diabetes mellitus, and prevention |
|
✓ |
| Radley et al [62] | Journal of American Medical Informatics Association | Systematic literature review and random-effects meta-analytic techniques, American Society of Health System Pharmacists Annual survey (2007), AHA Annual Survey (2007), and AHA EHR Adoption Database supplement (2008) | To analyze the adoption of CPOE systems on the reduction in medication errors in hospitals | Likelihood of medication errors |
|
✓ |
| Rao et al [63] | Journal of American Medical Informatics Association | Mailed surveys to a nationally representative random sample of practicing physicians from the Physician Masterfile of the American Medical Association (n=2769) | To analyze variation in the adoption of EHR functionalities and their use patterns, barriers to adoption, and perceived benefits by physician practice size | Physician perceptions of quality of clinical decision, quality of communication with patients and other providers, delivery of preventive or chronic care that met the guidelines, avoiding medication errors and prescription refills |
|
✓ |
| Risko et al [64] | Healthcare | Patient processing metrics (n=374 observations) were collected for ED physicians (34 physicians) at 2 hospitals for 7 months before and 10 months after EHR implementation | To analyze the impact of EHR implementation on ED physician efficiency and patient throughput | Patient workup times and LOS |
|
✓ |
| Ryan et al [65] | Medical Care | Data collected from 143 practices with EHR implementation (2009-2011) | To analyze whether EHR implementation and complementary interventions, such as clinical decision support, technical assistance, and financial incentives improved, the quality of care provided | Quality of care was analyzed from 8 separate indicators; 4 cardiovascular measures (smoking cessation intervention, BP control, cholesterol control, and aspirin or antithrombotic treatment) and 4 additional clinically important measures (BMI measurement, HbA1c control, pneumococcal vaccine, and asthma control) |
|
✓ |
| Schreiber and Shaha [66] | Journal of Innovation in Health Informatics | Data collected from a community hospital for 5 years after CPOE adoption | To evaluate whether an increase in adoption of CPOE leads to a decrease in LOS | LOS and cost measured by LOS | ✓ | ✓ |
| Scott et al [67] | The Journal of Bone and Joint Surgery | Data collected from an outpatient adult reconstruction clinic (n=143 patients) before implementing the hospital system–wide EMR system and 2 months, 6 months, and 2 years after implementation | To evaluate the impact of EMR implementation using advanced cost-accounting methods on orthopedic surgeons in an outpatient setting | Labor cost, documentation time for providers, and time spent interacting with patients | ✓ | ✓ |
| Shen et al [68] | International Journal of Healthcare Technology and Management | National Inpatient Sample and AHA EHR implementation survey for the year 2009 | To examine how EHR adoption affected the cost of care and quality outcomes in an acute care hospital setting | Cost of care for the 8 quality indicators (cardiovascular and cerebrovascular) and quality indicators for 5 cardiovascular and 3 cerebrovascular conditions and procedures | ✓ | ✓ |
| Silow-Carroll et al [69] | Issue Brief (Commonwealth Fund) | Interviews with individuals in the 9 hospitals that implemented a comprehensive EHR system | To analyze the experience of 9 hospitals in using EHR to improve quality and efficiency | Communication among providers, care coordination, patient engagement, and medical errors |
|
✓ |
| Singh et al [70] | Journal of American Medical Association Ophthalmology | Retrospective case-control study comparing the pre- (n=13,969 patient encounters) and post-EHR (n=14,191 patient encounters) implementation periods at an eye institute | To evaluate the impact of EHR system implementation from clinical and economic perspectives at a large multidisciplinary ophthalmic practice | Net revenue, revenue to volume ratio, capital and implementation costs, EHR incentive payments received, patient volume, diagnostic and procedure volume, and coding volumes | ✓ | ✓ |
| Sockolow et al [30] | Applied Clinical Informatics | Pre- and postobservational mixed methods study, Philadelphia-based homecare agency with 137 clinicians—data included clinician EHR documentation completion, EHR use data, Medicare billing data, an EHR Nurse Satisfaction survey, clinician observations, clinician interviews, and patient outcomes | To compare workflows, financial billing, and patient outcomes before and after implementation to analyze the effect of a homecare point of care EHR | Number of days required to create a financial reimbursement bill, productivity, behavioral outcomes, and clinicians’ perceptions of patient safety | ✓ | ✓ |
| Thirukumaran et al [71] | Health Services Research | Data collected from the SCIP Core Measure data set from the CMS Hospital Inpatient Quality Reporting (n=1816) program (March 2010 to February 2012) | To evaluate the effect of EHR placement on SCIP measures in a tertiary care teaching hospital | SCIP scores |
|
✓ |
| Tidwell et al [72] | Obstetrics and Gynecology | Data collected from an obstetrics and gynecology practice comprising 6 physicians and 6 midwives with 150 daily visits | To evaluate whether a low-cost electronic practice management system (EHR) can improve care coordination and financial measures | Net profit, days in accounts receivables, patient visits, no-show rate, and quality data gathering | ✓ | ✓ |
| Varpio et al [73] | Medical Education | A 2-phase longitudinal study; data collected through field observations (146 hours with 300 providers, 22 patients, and 32 patient family members), think-aloud (n=13) and think-after (n=11) sessions, interviews (n=39) and document retrieval (n=392) | To evaluate the impact of adopting EHR on clinician experience | Clinician experience was measured in terms of cognitive workload, clinical reasoning support mechanisms, and knowledge about the patient |
|
✓ |
| Walker-Czyz et al [74] | Journal of Nursing Administration | Data for a quantitative, retrospective analysis collected from urban hospitals (431 beds) with 10 medical-surgical units and 2 critical care units | To evaluate how an integrated EHR innovation adoption affects cost, nurse satisfaction, and nursing care delivered in terms of quality | Cost (nurse hours per patient day, nurse turnover, and nurse overtime), quality nursing care outcomes (hospital-acquired falls and pressure ulcers, ventilator-associated pneumonia, central line–associated bloodstream infections, and catheter-associated urinary tract infections) | ✓ | ✓ |
| Wang et al [75] | Preventing Chronic Disease | Clinical quality measure performance data collected from 151 primary care practices that implemented EHR (October 2009 to October 2011) | To analyze how clinical quality measures for independent primary care practices improve as a result of EHR use and technical support from a local public health agency | 4 key quality measures: antithrombotic therapy, BP control, HbA1c testing, and smoking cessation intervention |
|
✓ |
| Wang et al [26] | International Journal of Accounting Information Systems | Definitive health care data set for hospital-level data for the years 2011 to 2016 (n=3266 observations) | To evaluate how HIT expenses and intermediate business processes affect hospital financial performance and productivity | Return on assets, productivity ([net revenue, 1 million]), and number of staff beds) | ✓ | ✓ |
| Xiao et al [76] | Perspectives in Health Information Management | Charts were reviewed to collect data from a large tertiary public medical center (3 years before and 3 years after EHR implementation in July 2009) | To describe how electronic charting implementation in a large public outpatient clinic improves clinical documentation | Note completion and documentation of medication |
|
✓ |
| Yeung [16] | International Journal of Medical Informatics | 433 local health departments’ population-based data for 433 counties | To determine the impact of the adoption of EHR and health information exchange changes by local health departments on population health | The health of a population at the county level, as measured by health outcomes such as premature death and health-related quality of life |
|
✓ |
| Wani and Malhotra [77] | Journal of Operations Management | Acute care hospitals in California | To analyze the impact of EHR adoption in terms of full adoption vs meaningful assimilation on clinical outcomes | LOS and readmission rates |
|
✓ |
| Zhou et al [78] | Journal of the American Medical Informatics Association | To evaluate the extent of EHR use and how the quality of care delivered in ambulatory care practices varied according to the duration of EHR availability | Quality measures are aggregated into 6 clinical categories (asthma care, behavioral and mental health, cancer screening, diabetes care, well-child and adolescent visits, and women’s health screenings) | Quality measures aggregated into 6 clinical categories (asthma care, behavioral and mental health, cancer screening, diabetes care, well child and adolescent visit, women’s health screenings) | ✓ |
|
aEHR: electronic health record.
bCMS: Centers for Medicare and Medicaid Services.
c✓: indicates that the outcome was discussed in the study.
dHIT: health IT.
eAHRQ: Agency for Healthcare Research and Quality.
fEMR: electronic medical record.
gSCIP: Surgical Care Improvement Project.
hHIMSS: Healthcare Information and Management Systems Society.
iHbA1c: hemoglobin A1c.
jAHA: American Hospital Association.
kED: emergency department.
lLOS: length of stay.
mMICU: medical intensive care unit.
nCPOE: certified provider order entry.
oBP: blood pressure.