Meaningful use of electronic health record systems and process quality of care: evidence from a panel data analysis of U.S. acute-care hospitals

Impact of CPOE usage on medication management process costs and quality outcomes

We assess the impact of computerized physician order entry (CPOE) systems usage on cost and process quality in the medication management process. Data are compiled from 1,014 U.S. acute-care hospitals that have already implemented CPOE. Data sources include the American Hospital Association, HIMSS Analytics, and the Centers for Medicare and Medicaid Services. We examine the association of CPOE usage with nursing and pharmacy salary costs, and evidence-based medication process compliance. Empirical findings controlling for endogeneity in usage show that benefits accrue even when 100 percent usage is not achieved. We demonstrate that the relationship of CPOE usage with cost and compliance is non-linear.

The double-edged sword of electronic health records: implications for patient disclosure

OBJECTIVE

Electronic health record (EHR) systems are linked to improvements in quality of care, yet also privacy and security risks. Results from research studies are mixed about whether patients withhold personal information from their providers to protect against the perceived EHR privacy and security risks. This study seeks to reconcile the mixed findings by focusing on whether accounting for patients' global ratings of care reveals a relationship between EHR provider-use and patient non-disclosure.

MATERIALS AND METHODS

A nationally representative sample from the 2012 Health Information National Trends Survey was analyzed using bivariate and multivariable logit regressions to examine whether global ratings of care suppress the relationship between EHR provider-use and patient non-disclosure.

RESULTS

13% of respondents reported having ever withheld information from a provider because of privacy/security concerns. Bivariate analysis showed that withholding information was unrelated to whether respondents' providers used an EHR. Multivariable analysis showed that accounting for respondents' global ratings of care revealed a positive relationship between having a provider who uses an EHR and withholding information.

DISCUSSION

After accounting for global ratings of care, findings suggest that patients may non-disclose to providers to protect against the perceived EHR privacy and security risks. Despite evidence that EHRs inhibit patient disclosure, their advantages for promoting quality of care may outweigh the drawbacks.

CONCLUSIONS

Clinicians should leverage the EHR's value in quality of care and discuss patients' privacy concerns during clinic visits, while policy makers should consider how to address the real and perceived privacy and security risks of EHRs.

Use of Electronic Health-Related Datasets in Nursing and Health-Related Research

Datasets of gigabyte size are common in medical sciences. There is increasing consensus that significant untapped knowledge lies hidden in these large datasets. This review article aims to discuss Electronic Health-Related Datasets (EHRDs) in terms of types, features, advantages, limitations, and possible use in nursing and health-related research. Major scientific databases, MEDLINE, ScienceDirect, and Scopus, were searched for studies or review articles regarding using EHRDs in research. A total number of 442 articles were located. After application of study inclusion criteria, 113 articles were included in the final review. EHRDs were categorized into Electronic Administrative Health-Related Datasets and Electronic Clinical Health-Related Datasets. Subcategories of each major category were identified. EHRDs are invaluable assets for nursing the health-related research. Advanced research skills such as using analytical softwares, advanced statistical procedures, dealing with missing data and missing variables will maximize the efficient utilization of EHRDs in research.

The impact of electronic health record implementation and use on performance of the Surgical Care Improvement Project measures

OBJECTIVE

To examine the impact of electronic health record (EHR) deployment on Surgical Care Improvement Project (SCIP) measures in a tertiary-care teaching hospital.

DATA SOURCES

SCIP Core Measure dataset from the CMS Hospital Inpatient Quality Reporting Program (March 2010 to February 2012).

STUDY DESIGN

One-group pre- and post-EHR logistic regression and difference-in-differences analyses.

PRINCIPAL FINDINGS

Statistically significant short-term declines in scores were observed for the composite, postoperative removal of urinary catheter and post-cardiac surgery glucose control measures. A statistically insignificant improvement in scores for these measures was noted 3 months after EHR deployment.

CONCLUSION

The transition to an EHR appears to be associated with a short-term decline in quality. Implementation strategies should be developed to preempt or minimize this initial decline.

Does health information exchange reduce redundant imaging? Evidence from emergency departments

BACKGROUND

Broad-based electronic health information exchange (HIE), in which patients' clinical data follow them between care delivery settings, is expected to produce large quality gains and cost savings. Although these benefits are assumed to result from reducing redundant care, there is limited supporting empirical evidence.

OBJECTIVE

To evaluate whether HIE adoption is associated with decreases in repeat imaging in emergency departments (EDs).

DATA SOURCE/STUDY SETTING

ED discharge data from the State Emergency Department Databases for California and Florida for 2007-2010 were merged with Health Information Management Systems Society data that report hospital HIE participation.

METHODS

Using regression with ED fixed effects and trends, we performed a retrospective analysis of the impact of HIE participation on repeat imaging, comparing 37 EDs that initiated HIE participation during the study period to 410 EDs that did not participate in HIE during the same period. Within 3 common types of imaging tests [computed tomography (CT), ultrasound, and chest x-ray), we defined a repeat image for a given patient as the same study in the same body region performed within 30 days at unaffiliated EDs.

RESULTS

In our sample there were 20,139 repeat CTs (representing 14.7% of those cases with CT in the index visit), 13,060 repeat ultrasounds (20.7% of ultrasound cases), and 29,703 repeat chest x-rays (19.5% of x-ray cases). HIE was associated with reduced probability of repeat ED imaging in all 3 modalities: -8.7 percentage points for CT [95% confidence interval (CI): -14.7, -2.7], -9.1 percentage points for ultrasound (95% CI: -17.2, -1.1), and -13.0 percentage points for chest x-ray (95% CI: -18.3, -7.7), reflecting reductions of 44%-67% relative to sample means.

CONCLUSIONS

HIE was associated with reduced repeat imaging in EDs. This study is among the first to find empirical support for this anticipated benefit of HIE.

Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

OBJECTIVES

Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes.

SETTING

A regional cancer centre in Australia.

PARTICIPANTS

Disease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data.

PRIMARY AND SECONDARY OUTCOME MEASURES

Survival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC).

RESULTS

The ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours.

CONCLUSIONS

Machine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems.

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

Objective Readmission rates are high following acute myocardial infarction (AMI), but risk stratification has proved difficult because known risk factors are only weakly predictive. In the present study, we applied hospital data to identify the risk of unplanned admission following AMI hospitalisations. Methods The study included 1660 consecutive AMI admissions. Predictive models were derived from 1107 randomly selected records and tested on the remaining 553 records. The electronic medical record (EMR) model was compared with a seven-factor predictive score known as the HOSPITAL score and a model derived from Elixhauser comorbidities. All models were evaluated for the ability to identify patients at high risk of 30-day ischaemic heart disease readmission and those at risk of all-cause readmission within 12 months following the initial AMI hospitalisation. Results The EMR model has higher discrimination than other models in predicting ischaemic heart disease readmissions (area under the curve (AUC) 0.78; 95% confidence interval (CI) 0.71–0.85 for 30-day readmission). The positive predictive value was significantly higher with the EMR model, which identifies cohorts that were up to threefold more likely to be readmitted. Factors associated with readmission included emergency department attendances, cardiac diagnoses and procedures, renal impairment and electrolyte disturbances. The EMR model also performed better than other models (AUC 0.72; 95% CI 0.66–0.78), and with greater positive predictive value, in identifying 12-month risk of all-cause readmission. Conclusions Routine hospital data can help identify patients at high risk of readmission following AMI. This could lead to decreased readmission rates by identifying patients suitable for targeted clinical interventions. What is known about the topic? Many clinical and demographic risk factors are known for hospital readmissions following acute myocardial infarction, including multivessel disease, high baseline heart rate, hypertension, diabetes, obesity, chronic obstructive pulmonary disease and psychiatric morbidity. However, combining these risk factors into indices for predicting readmission had limited success. A recent study reported a C-statistic of 0.73 for predicting 30-day readmissions. In a recent American study, a simple seven-factor score was shown to predict hospital readmissions among medical patients. What does this paper add? This paper presents a way to predict readmissions following myocardial infarction using routinely collected administrative data. The model performed better than the recently described HOSPITAL score and a model derived from Elixhauser comorbidities. Moreover, the model uses only data generally available in most hospitals. What are the implications for practitioners? Routine hospital data available at discharges can be used to tailor preventative care for AMI patients, to improve institutional performance and to decrease the cost burden associated with AMI.