Effect of electronic records on mortality among patients in hospital and primary healthcare settings: a systematic review and meta-analyses

Background

Electronic medical records or electronic health records, collectively called electronic records, have significantly transformed the healthcare system and service provision in our world. Despite a number of primary studies on the subject, reports are inconsistent and contradictory about the effects of electronic records on mortality. Therefore, this review examined the effect of electronic records on mortality.

Methods

The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 guideline. Six databases: PubMed, EMBASE, Scopus, CINAHL, Cochrane Library, and Google Scholar, were searched from February 20 to October 25, 2023. Studies that assessed the effect of electronic records on mortality and were published between 1998 and 2022 were included. Joanna Briggs Institute quality appraisal tool was used to assess the methodological quality of the studies. Narrative synthesis was performed to identify patterns across studies. Meta-analysis was conducted using fixed effect and random-effects models to estimate the pooled effect of electronic records on mortality. Funnel plot and Egger's regression test were used to assess for publication bias.

Results

Fifty-four papers were found eligible for the systematic review, of which 42 were included in the meta-analyses. Of the 32 studies that assessed the effect of electronic health record on mortality, eight (25.00%) reported a statistically significant reduction in mortality, 22 (68.75%) did not show a statistically significant difference, and two (6.25%) studies reported an increased risk of mortality. Similarly, among the 22 studies that determined the effect of electronic medical record on mortality, 12 (54.55%) reported a statistically significant reduction in mortality, and ten (45.45%) studies didn't show a statistically significant difference. The fixed effect and random effects on mortality were OR = 0.95 (95% CI: 0.93-0.97) and OR = 0.94 (95% CI: 0.89-0.99), respectively. The associated I-squared was 61.5%. Statistical tests indicated that there was no significant publication bias among the studies included in the meta-analysis.

Conclusion

Despite some heterogeneity among the studies, the review indicated that the implementation of electronic records in inpatient, specialized and intensive care units, and primary healthcare facilities seems to result in a statistically significant reduction in mortality. Maturity level and specific features may have played important roles.

Systematic Review Registration

PROSPERO (CRD42023437257).

The Impact of Electronic Health Records and Meaningful Use on Inpatient Quality

ABSTRACT

It is unclear if national investments of the HITECH Act have resulted in significant improvements in care processes and outcomes by making "Meaningful Use (MU)" of Electronic Health Record (EHR) systems. The objective of this study is to determine the impact of EHRs and MU on inpatient quality. We used inpatient hospitalization data, American Hospital Association annual survey, and the Centers for Medicare and Medicaid Services attestation records to study the impact of EHRs on inpatient quality composite scores. Agency for Healthcare Research and Quality Inpatient Quality Indicator (IQI) software version 5.0 was used to compute the hospital-level risk-adjusted standardized rates for IQI indicators and composite scores. After adjusting for confounding factors, EHRs that attested to MU had a positive impact on IQI 90 and IQI 91 composite scores with an 8% decrease in composites for mortality for selected procedures and 18% decrease in composites for mortality for selected conditions. Meaningful Use attestation may be an important driver related to inpatient quality. Health care leaders may need to focus on quality improvement initiatives and advanced analytics to better leverage their EHRs to improve IQI 90 composite score for mortality for selected procedures, because we observed a lesser impact on IQI 90 compared with IQI 91.

Value of the Electronic Medical Record for Hospital Care: Update From the Literature

Background

Electronic records could improve quality and efficiency of health care. National and international bodies propagate this belief worldwide. However, the evidence base concerning the effects and advantages of electronic records is questionable. The outcome of health care systems is influenced by many components, making assertions about specific types of interventions difficult. Moreover, electronic records itself constitute a complex intervention offering several functions with possibly positive as well as negative effects on the outcome of health care systems.

Objective

The aim of this review is to summarize empirical studies about the value of electronic medical records (EMRs) for hospital care published between 2010 and spring 2019.

Methods

The authors adopted their method from a series of literature reviews. The literature search was performed on MEDLINE with “Medical Record System, Computerized” as the essential keyword. The selection process comprised 2 phases looking for a consent of both authors. Starting with 1345 references, 23 were finally included in the review. The evaluation combined a scoring of the studies’ quality, a description of data sources in case of secondary data analyses, and a qualitative assessment of the publications’ conclusions concerning the medical record’s impact on quality and efficiency of health care.

Results

The majority of the studies stemmed from the United States (19/23, 83%). Mostly, the studies used publicly available data (“secondary data studies”; 17/23, 74%). A total of 18 studies analyzed the effect of an EMR on the quality of health care (78%), 16 the effect on the efficiency of health care (70%). The primary data studies achieved a mean score of 4.3 (SD 1.37; theoretical maximum 10); the secondary data studies a mean score of 7.1 (SD 1.26; theoretical maximum 9). From the primary data studies, 2 demonstrated a reduction of costs. There was not one study that failed to demonstrate a positive effect on the quality of health care. Overall, 9/16 respective studies showed a reduction of costs (56%); 14/18 studies showed an increase of health care quality (78%); the remaining 4 studies missed explicit information about the proposed positive effect.

Conclusions

This review revealed a clear evidence about the value of EMRs. In addition to an awesome majority of economic advantages, the review also showed improvements in quality of care by all respective studies. The use of secondary data studies has prevailed over primary data studies in the meantime. Future work could focus on specific aspects of electronic records to guide their implementation and operation.

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.

Using a Relational Database to Improve Mortality and Length of Stay for a Department of Surgery: A Comparative Review of 5200 Patients

The emphasis on high-quality care has spawned the development of quality programs, most of which focus on broad outcome measures across a diverse group of providers. Our aim was to investigate the clinical outcomes for a department of surgery with multiple service lines of patient care using a relational database. Mortality, length of stay (LOS), patient safety indicators (PSIs), and hospital-acquired conditions were examined for each service line. Expected values for mortality and LOS were derived from University HealthSystem Consortium regression models, whereas expected values for PSIs were derived from Agency for Healthcare Research and Quality regression models. Overall, 5200 patients were evaluated from the months of January through May of both 2011 (n = 2550) and 2012 (n = 2650). The overall observed-to-expected (O/E) ratio of mortality improved from 1.03 to 0.92. The overall O/E ratio for LOS improved from 0.92 to 0.89. PSIs that predicted mortality included postoperative sepsis (O/E:1.89), postoperative respiratory failure (O/E:1.83), postoperative metabolic derangement (O/E:1.81), and postoperative deep vein thrombosis or pulmonary embolus (O/E:1.8). Mortality and LOS can be improved by using a relational database with outcomes reported to specific service lines. Service line quality can be influenced by distribution of frequent reports, group meetings, and service line-directed interventions.

Effect of Electronic Health Record Implementation in Critical Care on Survival and Medication Errors

BACKGROUND

Electronic health records (EHR) with computerized physician order entry have become exceedingly common and government incentives have urged implementation. The purpose of this study was to ascertain the effect of EHR implementation on medical intensive care unit (MICU) mortality, length of stay (LOS), hospital LOS and medication errors.

MATERIALS AND METHODS

Prospective, observational study from July 2010-June 2011 in MICU at an urban teaching hospital in Atlanta, Georgia of 797 patients admitted to the MICU; 281 patients before the EHR implementation and 516 patients post-EHR implementation.

RESULTS

Compared with the preimplementation period (N = 43 per 281), the mortality risk at 4 months post-EHR implementation (N = 41 per 247) and at 8 months post-EHR implementation (N = 26 per 269) significantly decreased (P < 0.001). In addition, the mean MICU LOS statistically decreased from 4.03 ± 1.06 days pre-EHR to 3.26 ± 1.06 days 4 months post-EHR and to 3.12 ± 1.05 days 8 months post-EHR (P = 0.002). However, the mean hospital LOS was not statistically decreased. Although medication errors increased after implementation (P = 0.002), this was attributable to less severe errors and there was actually a decrease in the number of severe medication errors (both P < 0.001).

CONCLUSIONS

We report a survival benefit following the implementation of EHR with computerized physician order entry in a critical care setting and a concomitant decrease in the number of severe medication errors. Although overall hospital LOS was not shortened, this study proposes that EHR implementation in a busy urban hospital was associated with improved ICU outcomes.

Impact of the Electronic Medical Record on Mortality, Length of Stay, and Cost in the Hospital and ICU: A Systematic Review and Metaanalysis

OBJECTIVE

To evaluate effects of health information technology in the inpatient and ICU on mortality, length of stay, and cost. Methodical evaluation of the impact of health information technology on outcomes is essential for institutions to make informed decisions regarding implementation.

DATA SOURCES

EMBASE, Scopus, Medline, the Cochrane Review database, and Web of Science were searched from database inception through July 2013. Manual review of references of identified articles was also completed.

STUDY SELECTION

Selection criteria included a health information technology intervention such as computerized physician order entry, clinical decision support systems, and surveillance systems, an inpatient setting, and endpoints of mortality, length of stay, or cost. Studies were screened by three reviewers. Of the 2,803 studies screened, 45 met selection criteria (1.6%).

DATA EXTRACTION

Data were abstracted on the year, design, intervention type, system used, comparator, sample sizes, and effect on outcomes. Studies were abstracted independently by three reviewers.

DATA SYNTHESIS

There was a significant effect of surveillance systems on in-hospital mortality (odds ratio, 0.85; 95% CI, 0.76-0.94; I=59%). All other quantitative analyses of health information technology interventions effect on mortality and length of stay were not statistically significant. Cost was unable to be quantitatively evaluated. Qualitative synthesis of studies of each outcome demonstrated significant study heterogeneity and small clinical effects.

CONCLUSIONS

Electronic interventions were not shown to have a substantial effect on mortality, length of stay, or cost. This may be due to the small number of studies that were able to be aggregately analyzed due to the heterogeneity of study populations, interventions, and endpoints. Better evidence is needed to identify the most meaningful ways to implement and use health information technology and before a statement of the effect of these systems on patient outcomes can be made.

An alarming presentation of Creutzfeldt-Jakob disease following a self-inflicted gunshot wound to the head

Transmissible spongiform encephalopathies (TSE), also known as prion diseases, are characterized by rapid and fatal neurological decline. They not only detrimentally affect the patient, but also present additional challenges to healthcare systems due to the infectivity of the tissues and the difficulty of inactivating the prion. The most common TSE is Creutzfeldt-Jakob disease (CJD), which can occur after familial, spontaneous or acquired transmission. TSEs received more attention after the development of variant CJD (vCJD), also known as Mad Cow Disease, in the UK during the mid-1990s. Unlike familial or spontaneous CJD, this variant was connected to consumption of cattle contaminated with the prion disease, bovine spongiform encephalopathy.This development increased interest in the etiology of CJD and other TSEs and the risk it presents as an infectious disease. The following details the case of a 59-year-old male infected with CJD presented to our level II trauma center for treatment following a self-inflicted gunshot wound to the head.

Impact of Implementing an Electronic Health Record on Surgical Resident Work Flow, Duty Hours, and Operative Experience

Our objective was to assess the effect of implementing an electronic health record (EHR) on surgical resident work flow, duty hours, and operative experience at a large teaching hospital. In May 2012, an EHR was put into effect at our institution replacing paper documentation and orders. Resident time to complete patient documentation, average duty hours, and operative experience before EHR and afterward (at 1, 4, 6, 8, and 24 weeks) were surveyed. We obtained 100 per cent response rate from 15 surgical residents at all time intervals. The average time spent documenting before EHR was 9 ± 2 minutes per patient document and at Weeks 1, 4, 6, 8, and 24 after EHR implementation was 22 ± 10, 15 ± 7, 15 ± 7, 14 ± 8, and 12 ± 4 minutes, respectively. Repeated measures analysis of variance demonstrated a difference among the means ( P < 0.0001). Discharge summary and operative note remained significantly longer to complete at Week 24 compared with paper documentation ( P < 0.05). Average resident work hours and operative cases per week before EHR were 77 ± 5 hours and 12 ± 5 cases, respectively, which were similar at all time points after EHR implementation ( P > 0.05). At 24 weeks after EHR, 74 per cent of residents felt their risk of performing a medical error using electronic documentation and order entry was higher compared with paper charting and orders. Transition to EHR led to a significant doubling in resident time spent performing documentation for each patient. It improved over 6 months after implementation but never reached the pre-EHR baseline for operative notes and discharge summaries. Average resident work hours and case logs remained similar during this transition.

The effect of electronic medical record application on the length of stay in a Chinese general hospital: a department- and disease-focused interrupted time-series study

A key purpose of electronic medical records (EMR) introduced in medical institutions is to improve work efficiency. The average length of stay (LOS) is just an important indicator to evaluate work efficiency of medical care in hospitals. Recently, there have been reports about effects of EMR application on LOS in medical institutions, but they have been mostly based on the overall analysis of a region or a hospital and not of specific clinical departments and diseases or based on longer time periods. Therefore, in this study, we selected four clinical departments and four diseases with the largest number of inpatients from January 2004 to December 2012 in a Chinese 3A general hospital and used an interrupted time-series method by the departments and diseases to analyze the relationship of EMR application and LOS. Through our analyses, we concluded that, under unadjusted condition, LOS were all reduced (P < 0.001) after EMR application in four departments and for four diseases. After adjustment by gender, age or admission condition, LOS still all decreased after EMR application (P < 0.05) regardless of departments or diseases. The trend changes in LOS reversed from increasing to decreasing in the orthopedics department (coefficient: 0.016 to -0.079), the cardiovascular surgery department (coefficient: 0.007 to -0.126) and all departments overall (coefficient: 0.004 to -0.070), as well as for the intervertebral disc disorders (coefficient: 0.026 to -0.068). Furthermore, the decreasing trend gained a larger slope in the cardiology department (coefficient: -0.017 to -0.023), the neurology department (coefficient: -0.012 to -0.043) and for the coronary heart disease (coefficient: -0.010 to -0.018), the ventricular septal defect (coefficient: -0.024 to -0.059), and the cerebral infarction (coefficient: -0.031 to -0.040). Together, these findings indicate that EMR application coincided with a decrease in LOS and may have a contribution to the decrease.

Potential Social, Environmental, and Regulatory Threats to Electronic Health Record Strategies for Improving Tobacco Treatment in Healthcare

Introduction– The potential impact of electronic health records (EHR) in driving tobacco treatment behaviours within healthcare settings has been established. However, little is known about the administrative variables that may undermine effectiveness in real world settings.

Aims– Assist healthcare planners interested in implementing tobacco-EHR systems by identifying an EHR framework that is consistent with published treatment guidelines, and the important organisational variables that can undermine the effectiveness of tobacco-EHR.

Methods– This paper considers the established literature on EHR implementation and physician behaviour change, and integrates this understanding with the observations of an expert workgroup tasked with facilitating tobacco-EHR implementation in Southeastern Pennsylvania.

Results/ Findings– System change in this topic area will continue to be problematic unless attention is paid to several important lessons regarding: 1) the evolving healthcare regulatory environment, 2) the integration of tobacco use treatment into primary care, and 3) the existing social and organisational barriers to uptake of evidence-based recommendations.

Conclusion– Healthcare organisations seeking to reduce the impact of tobacco use on their patients are well served by tobacco-EHR systems that improve care. Managers can avoid sub-optimal implementation by considering several threats to effectiveness before proceeding to systems change.