Currently Viewing:
The American Journal of Managed Care Special Issue: Health Information Technology
Improving Adherence to Cardiovascular Disease Medications With Information Technology
William M. Vollmer, PhD; Ashli A. Owen-Smith, PhD; Jeffrey O. Tom, MD, MS; Reesa Laws, BS; Diane G. Ditmer, PharmD; David H. Smith, PhD; Amy C. Waterbury, MPH; Jennifer L. Schneider, MPH; Cyndee H. Yonehara, BS; Andrew Williams, PhD; Suma Vupputuri, PhD; and Cynthia S. Rand, PhD
Information Retrieval Pathways for Health Information Exchange in Multiple Care Settings
Patrick Kierkegaard, PhD; Rainu Kaushal, MD, MPH; and Joshua R. Vest, PhD, MPH
The 3 Key Themes in Health Information Technology
Julia Adler-Milstein, PhD
Leveraging EHRs to Improve Hospital Performance: The Role of Management
Julia Adler-Milstein, PhD; Kirstin Woody Scott, MPhil; and Ashish K. Jha, MD, MPH
Electronic Alerts and Clinician Turnover: The Influence of User Acceptance
Sylvia J. Hysong, PhD; Christiane Spitzmuller, PhD; Donna Espadas, BS; Dean F. Sittig, PhD; and Hardeep Singh, MD, MPH
Cost Implications of Human and Automated Follow-up in Ambulatory Care
Eta S. Berner, EdD; Jeffrey H. Burkhardt, PhD; Anantachai Panjamapirom, PhD; and Midge N. Ray, MSN, RN
Primary Care Capacity as Insurance Coverage Expands: Examining the Role of Health Information Technology
Renuka Tipirneni, MD, MSc; Ezinne G. Ndukwe, MPH; Melissa Riba, MS; HwaJung Choi, PhD; Regina Royan, MPH; Danielle Young, MPH; Marianne Udow-Phillips, MHSA; and Matthew M. Davis, MD, MAPP
Adoption of Electronic Prescribing for Controlled Substances Among Providers and Pharmacies
Meghan Hufstader Gabriel, PhD; Yi Yang, MD, PhD; Varun Vaidya, PhD; and Tricia Lee Wilkins, PharmD, PhD
Health Information Exchange and the Frequency of Repeat Medical Imaging
Joshua R. Vest, PhD, MPH; Rainu Kaushal, MD, MPH; Michael D. Silver, MS; Keith Hentel, MD, MS; and Lisa M. Kern, MD
Currently Reading
Information Technology and Hospital Patient Safety: A Cross-Sectional Study of US Acute Care Hospitals
Ajit Appari, PhD; M. Eric Johnson, PhD; and Denise L. Anthony, PhD
Trending Health Information Technology Adoption Among New York Nursing Homes
Erika L. Abramson, MD, MS; Alison Edwards, MS; Michael Silver, MS; Rainu Kaushal, MD, MPH; and the HITEC investigators
Electronic Health Record Availability Among Advanced Practice Registered Nurses and Physicians
Janet M. Coffman, PhD, MPP, MA; Joanne Spetz, PhD; Kevin Grumbach, MD; Margaret Fix, MPH; and Andrew B. Bindman, MD
The Value of Health Information Technology: Filling the Knowledge Gap
Robert S. Rudin, PhD; Spencer S. Jones, PhD; Paul Shekelle, MD, PhD; Richard J. Hillestad, PhD; and Emmett B. Keeler, PhD
Overcoming Barriers to a Research-Ready National Commercial Claims Database
David Newman, JD, PhD; Carolina-Nicole Herrera, MA; and Stephen T. Parente, PhD
The Effects of Health Information Technology Adoption and Hospital-Physician Integration on Hospital Efficiency
Na-Eun Cho, PhD; Jongwha Chang, PhD; and Bebonchu Atems, PhD

Information Technology and Hospital Patient Safety: A Cross-Sectional Study of US Acute Care Hospitals

Ajit Appari, PhD; M. Eric Johnson, PhD; and Denise L. Anthony, PhD
Use of health information technology in acute care settings is associated with modestly lower rates of adverse patient safety outcomes for inpatient and surgical care.
To determine whether health information technology (IT) systems are associated with better patient safety in acute care settings.

Study Design
In a cross-sectional retrospective study, data on hospital patient safety performance for October 2008 to June 2010 were combined with 2007 information technology systems data. The sample included 3002 US non-federal acute care hospitals. Electronic health record (EHR) system was coded as a composite dichotomous variable based on the presence of 10 major clinical and administrative applications that (if in use) could potentially meet stage 1 “meaningful use” objectives. The surgical IT system was measured as a dichotomous variable if a hospital used at least 1 of the perioperative, preoperative, or postoperative information systems. Hospital patient safety performance was measured by risk-standardized estimated rates per 1000 admissions. Statistical analyses were conducted using an estimated dependent variable methodology with gamma-log link–based weighted generalized linear models, adjusting for hospital characteristics, historical composite process quality, and propensity for EHR adoption.

We found that the use of surgical IT systems was associated with 7% to 26% lower rates for 7 of 8 patient safety indicators (incidence rate ratio [IRR] range from 0.74 to 0.93; all P values <.01). Further, stage 1 meaningful use-capable EHR systems were associated with 7% to 11% lower rates on 3 of 8 measures (IRR range from 0.89 to 0.93; all P values <.01).

Our results suggest that the use of IT is associated with modestly lower rates of adverse events in hospitals. However, the cross-sectional design limits our ability to make causal conclusions.

Am J Manag Care. 2014;20(11 Spec No. 17):eSP39-eSP47
A retrospective cross-sectional analysis of a large national sample of nonfederal acute care hospitals suggests that the use of health information technology (IT)—specifically surgical IT systems, and electronic health record (EHR) systems capable of meeting Stage 1 meaningful use requirements—is associated with moderate, but statistically significant, reductions in adverse patient safety outcomes.
  • Hospitals using surgical IT had lower relative rates on 7 of 8 patient safety indicators while those using Stage 1 EHRs had lower rates on 3 measures.
  • Health IT including surgical systems and Stage 1-capable EHRs could likely benefit hospitals seeking to improve patient safety.
Patient safety remains a major challenge in the US healthcare system, recently drawing renewed attention as a national priority.1 About one-third of hospitalized patients experience adverse events,2 and these rates are alarmingly higher at high-quality hospitals.2,3 The HHS recently announced a $1-billion national initiative, Partnership for Patients, aimed at reducing preventable complications and hospital-acquired conditions by 40%, which could result in about 1.8 million fewer injuries and more than 60,000 saved lives over 3 years.4

Recognizing the potential role that health information technology (IT) could play in improving patient safety and quality of care,5,6 the Obama administration committed $27 billion to promote the implementation and meaningful use (MU) of electronic health records (EHRs).7 The widespread and effective use of health IT is expected to help foster an environment of safe, patient-centered care through improved clinical performance; access to timely, relevant clinical information; and better communication between and among caregivers and patients.

Especially in the context of surgical care, the EHR and functional systems like surgical IT (eg, perioperative systems, preoperative systems, and postoperative systems) can improve patient safety through multiple mechanisms such as providing timely and comprehensive health information that may prevent errors or allow for rapid corrections.8,9 For example, recent studies have shown the beneficial impact of health IT on safety outcomes, including timely discontinuation of postoperative antibacterials10; improved adherence to evidencebased guidelines11; enhanced work flow and management of surgical team members12; effective communication to all providers during transitions and across specific phases of care delivery12; and facilitating retrospective analysis of 3 adverse events to guide future improvement efforts.8 Despite such growing evidence of health IT benefits,13-20 recent systematic reviews raised concern over the paucity of generalizable evidence of health IT for patient safety and quality outcomes.21,22

In this cross-sectional retrospective study, we investigate the relationship between hospital IT systems and performance on a subset of Agency for Health Research and Quality (AHRQ) patient safety indicators (PSIs) that include various adverse events such as serious, but potentially preventable, complications related to inpatient medical or surgical care, and deaths for select treatments or conditions. Using a large national sample of nonfederal acute care hospitals, we observed that health IT is associated with modestly lower rates of adverse events. While to the best of our knowledge, this is one of the first studies to demonstrate evidence of a positive relationship between health IT and hospitals’ patient safety measures using national data, the cross-sectional design of this observational study does not allow us to draw causal conclusions about the relationship.


Data Sources

We combined data from 3 sources. Hospital performance data came from the October 2011 release of CMS Hospital Compare on 8 AHRQ indicators related to patient safety and inpatient quality outcomes. These data include facility-level risk-standardized rate estimates, adjusting for patient characteristics, for each measure along with 95% confidence intervals and the number of patients hospitalized (ie, “population” at risk) for each hospital during the sampling period of October 2008 to June 2010.23

Hospital IT systems data came from the 2008 release of the Health Information and Management Systems Society (HIMSS) Analytics Database, which includes hospital characteristics and the operational status of health IT achieved by the end of 2007. HIMSS is the most comprehensive database of hospital IT adoption decisions,24,25 and has been used extensively in health IT research.13,15,25 The HIMSS data are taken from 2007, while CMS hospital safety performance data are taken from the subsequent period (2008-2010) to avoid an overlap of the quality measurement period with the initial deployment of new technology.13,26 Lastly, data on hospitals’ organizational characteristics, used as control variables in our analyses, were obtained from the 2009 CMS Acute Inpatient Prospective Payment System Impact file and HIMSS. Our final sample included 3002 nonfederal acute care US hospitals.

Measurement of Surgical IT Systems

In hospital settings, several types of IT systems are deployed to manage and facilitate care delivery to surgical patients. In this study, we focus on 3 applications: perioperative, preoperative, and postoperative information systems. Perioperative systems provide clinical documentation and management of relevant real-time surgery procedures, and include functionalities such as clinical order management, decision support, anesthesia documentation, integration to anesthesia systems, smart cabinets, imaging systems, and potentially smart surgical instruments for image-guided surgery. It may also provide support for management of relevant operating room supplies and medications during surgery. Preoperative systems provide clinical documentation and management of relevant presurgery information and patient preparation for surgery. It also provides for the management of relevant presurgery room preparation, operating room supplies and medications, and staff. Postoperative systems provide clinical documentation and management of relevant follow-up procedures as well as transfers to step-down or intensive care units.

In this study, we constructed a linear composite of these 3 technologies as a dichotomous variable to indicate if a hospital had at least 1 of these 3 types of surgical IT systems in use as of 2007. While these 3 technologies may potentially influence patient care outcomes through different modalities, our focus is on the associative relationship between any such aggregate level IT capability supporting surgical care with the select set of AHRQ indicators.

Measurement of EHR MU Capability

Within the ambit of the federal incentive program, providers are expected to demonstrate the MU of EHR systems based on specific criteria set forth at various stages, with the first stage defined by the 2011 standards. Accomplishing these objectives requires the use of several EHR functionalities—for example, clinical decision support should provide for basic drug-drug, drug-allergy, and drug-formulary checks.27 Based on the functionalities required to demonstrate Stage 1 MU, 10 major clinical and administrative systems are needed. 13,27 These systems include admission/discharge/transfer systems; auxiliary information systems (laboratory, pharmacy, and radiology); e-prescribing; clinical data repository; clinical decision support; nursing documentation; electronic medication administration record; and computerized physician order entry systems. Hospitals were categorized as having an EHR system capable of 2011 MU functionality if they had all of the above applications in use by 2007, otherwise as not (serving as the reference group). While complete satisfaction of the 2011 MU objectives requires demonstrating routine clinical and administrative activities using the EHR system, here we measure only whether a hospital had the necessary functional capabilities to carry out those activities.

Measurement of Hospital Patient Safety Performance

Hospital patient safety performance was measured by 8 adverse event indicators developed by AHRQ. These indicators refer to serious but potentially preventable complications from inpatient medical or surgical care; and deaths from select treatment or conditions. These include 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 a 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).

Hospital Compare reports risk-standardized rates per 1000 patients at risk for each hospital facility based on Medicare Fee-for-Service claims data. Hospital Compare reports these rates based on prediction models implemented in AHRQ-PSI software and risk-adjusted for patient characteristics (age, gender), severity of illness, and 25 comorbidities as covariates (with associated 95% confidence intervals). The details of the risk-adjustment algorithm are described elsewhere.23 To ensure adequate reliability in this study, only those hospitals for which PSI rate estimates were based on at least 30 patients at risk were included in our analyses.28,29

Measurement of Hospital Characteristics

Care outcomes are affected by organizational structures, processes of care, and patient characteristics.30-32 Since the use of IT systems may also be correlated with hospital characteristics leading to selection bias, we estimated propensity scores for having an EHR system in use, employing data from the previous year (2007 HIMSS release), and then constructed dummy variables representing quintiles of EHR system propensity. Likewise, performance on patient safety measures may be influenced by overall hospital quality such that hospitals performing highly on process quality measures are expected to have fewer adverse events.33,34 To account for such confounding effects, we constructed dummy variables representing quintiles of facility-specific historical performance (2005-2007) on composite process quality scores for acute myocardial infarction, heart failure, pneumonia, and surgical care infection prevention.

We also used a comprehensive set of control variables to account for potential confounding effects: teaching status (academic and minor teaching hospitals); profit status; membership in a multihospital integrated delivery system; magnet status for nursing excellence; presence of cardiac intensive care unit; participation in stroke registry and nursing registry; having a Patient Safety Officer; staffed bed size; rural location; and whether the hospital qualified for Medicare disproportionate share payments.13,16,35 All hospital characteristic variables were operationalized as dichotomous variables, except staffed bed size which was categorized into 5 groups (6-99 beds, 100-199 beds, 200-299 beds, 300-399 beds, and 400+ beds).

Statistical Analysis

Copyright AJMC 2006-2020 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up