ICU nurses' acceptance of electronic health records

Objective

To assess intensive care unit (ICU) nurses'' acceptance of electronic health records (EHR) technology and examine the relationship between EHR design, implementation factors, and nurse acceptance.

Design

The authors analyzed data from two cross-sectional survey questionnaires distributed to nurses working in four ICUs at a northeastern US regional medical center, 3 months and 12 months after EHR implementation.

Measurements

Survey items were drawn from established instruments used to measure EHR acceptance and usability, and the usefulness of three EHR functionalities, specifically computerized provider order entry (CPOE), the electronic medication administration record (eMAR), and a nursing documentation flowsheet.

Results

On average, ICU nurses were more accepting of the EHR at 12 months as compared to 3 months. They also perceived the EHR as being more usable and both CPOE and eMAR as being more useful. Multivariate hierarchical modeling indicated that EHR usability and CPOE usefulness predicted EHR acceptance at both 3 and 12 months. At 3 months postimplementation, eMAR usefulness predicted EHR acceptance, but its effect disappeared at 12 months. Nursing flowsheet usefulness predicted EHR acceptance but only at 12 months.

Conclusion

As the push toward implementation of EHR technology continues, more hospitals will face issues related to acceptance of EHR technology by staff caring for critically ill patients. This research suggests that factors related to technology design have strong effects on acceptance, even 1 year following the EHR implementation.     

Are physicians' perceptions of healthcare quality and practice satisfaction affected by errors associated with electronic health record use?

Background

Electronic health record (EHR) adoption is a national priority in the USA, and well-designed EHRs have the potential to improve quality and safety. However, physicians are reluctant to implement EHRs due to financial constraints, usability concerns, and apprehension about unintended consequences, including the introduction of medical errors related to EHR use. The goal of this study was to characterize and describe physicians'' attitudes towards three consequences of EHR implementation: (1) the potential for EHRs to introduce new errors; (2) improvements in healthcare quality; and (3) changes in overall physician satisfaction.

Methods

Using data from a 2007 statewide survey of Massachusetts physicians, we conducted multivariate regression analysis to examine relationships between practice characteristics, perceptions of EHR-related errors, perceptions of healthcare quality, and overall physician satisfaction.

Results

30% of physicians agreed that EHRs create new opportunities for error, but only 2% believed their EHR has created more errors than it prevented. With respect to perceptions of quality, there was no significant association between perceptions of EHR-associated errors and perceptions of EHR-associated changes in healthcare quality. Finally, physicians who believed that EHRs created new opportunities for error were less likely be satisfied with their practice situation (adjusted OR 0.49, p=0.001).

Conclusions

Almost one third of physicians perceived that EHRs create new opportunities for error. This perception was associated with lower levels of physician satisfaction.     

How is the electronic health record being used? Use of EHR data to assess physician-level variability in technology use

Background

Studies of the effects of electronic health records (EHRs) have had mixed findings, which may be attributable to unmeasured confounders such as individual variability in use of EHR features.

Objective

To capture physician-level variations in use of EHR features, associations with other predictors, and usage intensity over time.

Methods

Retrospective cohort study of primary care providers eligible for meaningful use at a network of federally qualified health centers, using commercial EHR data from January 2010 through June 2013, a period during which the organization was preparing for and in the early stages of meaningful use.

Results

Data were analyzed for 112 physicians and nurse practitioners, consisting of 430 803 encounters with 99 649 patients. EHR usage metrics were developed to capture how providers accessed and added to patient data (eg, problem list updates), used clinical decision support (eg, responses to alerts), communicated (eg, printing after-visit summaries), and used panel management options (eg, viewed panel reports). Provider-level variability was high: for example, the annual average proportion of encounters with problem lists updated ranged from 5% to 60% per provider. Some metrics were associated with provider, patient, or encounter characteristics. For example, problem list updates were more likely for new patients than established ones, and alert acceptance was negatively correlated with alert frequency.

Conclusions

Providers using the same EHR developed personalized patterns of use of EHR features. We conclude that physician-level usage of EHR features may be a valuable additional predictor in research on the effects of EHRs on healthcare quality and costs.     

The impact of the electronic medical record on structure,process, and outcomes within primary care: a systematic review of the evidence

Background

The electronic medical record (EMR)/electronic health record (EHR) is becoming an integral component of many primary-care outpatient practices. Before implementing an EMR/EHR system, primary-care practices should have an understanding of the potential benefits and limitations.

Objective

The objective of this study was to systematically review the recent literature around the impact of the EMR/EHR within primary-care outpatient practices.

Materials and methods

Searches of Medline, EMBASE, CINAHL, ABI Inform, and Cochrane Library were conducted to identify articles published between January 1998 and January 2010. The gray literature and reference lists of included articles were also searched. 30 studies met inclusion criteria.

Results and discussion

The EMR/EHR appears to have structural and process benefits, but the impact on clinical outcomes is less clear. Using Donabedian''s framework, five articles focused on the impact on healthcare structure, 21 explored healthcare process issues, and four focused on health-related outcomes.     

Openness of patients' reporting with use of electronic records: psychiatric clinicians' views

Objectives

Improvements in electronic health record (EHR) system development will require an understanding of psychiatric clinicians'' views on EHR system acceptability, including effects on psychotherapy communications, data-recording behaviors, data accessibility versus security and privacy, data quality and clarity, communications with medical colleagues, and stigma.

Design

Multidisciplinary development of a survey instrument targeting psychiatric clinicians who recently switched to EHR system use, focus group testing, data analysis, and data reliability testing.

Measurements

Survey of 120 university-based, outpatient mental health clinicians, with 56 (47%) responding, conducted 18 months after transition from a paper to an EHR system.

Results

Factor analysis gave nine item groupings that overlapped strongly with five a priori domains. Respondents both praised and criticized the EHR system. A strong majority (81%) felt that open therapeutic communications were preserved. Regarding data quality, content, and privacy, clinicians (63%) were less willing to record highly confidential information and disagreed (83%) with including their own psychiatric records among routinely accessed EHR systems.

Limitations

single time point; single academic medical center clinic setting; modest sample size; lack of prior instrument validation; survey conducted in 2005.

Conclusions

In an academic medical center clinic, the presence of electronic records was not seen as a dramatic impediment to therapeutic communications. Concerns regarding privacy and data security were significant, and may contribute to reluctances to adopt electronic records in other settings. Further study of clinicians'' views and use patterns may be helpful in guiding development and deployment of electronic records systems.     

Normalized names for clinical drugs: RxNorm at 6?years

Objective

In the 6 years since the National Library of Medicine began monthly releases of RxNorm, RxNorm has become a central resource for communicating about clinical drugs and supporting interoperation between drug vocabularies.

Materials and methods

Built on the idea of a normalized name for a medication at a given level of abstraction, RxNorm provides a set of names and relationships based on 11 different external source vocabularies. The standard model enables decision support to take place for a variety of uses at the appropriate level of abstraction. With the incorporation of National Drug File Reference Terminology (NDF-RT) from the Veterans Administration, even more sophisticated decision support has become possible.

Discussion

While related products such as RxTerms, RxNav, MyMedicationList, and MyRxPad have been recognized as helpful for various uses, tasks such as identifying exactly what is and is not on the market remain a challenge.     

Toward personalizing treatment for depression: predicting diagnosis and severity

Objective

Depression is a prevalent disorder difficult to diagnose and treat. In particular, depressed patients exhibit largely unpredictable responses to treatment. Toward the goal of personalizing treatment for depression, we develop and evaluate computational models that use electronic health record (EHR) data for predicting the diagnosis and severity of depression, and response to treatment.

Materials and methods

We develop regression-based models for predicting depression, its severity, and response to treatment from EHR data, using structured diagnosis and medication codes as well as free-text clinical reports. We used two datasets: 35 000 patients (5000 depressed) from the Palo Alto Medical Foundation and 5651 patients treated for depression from the Group Health Research Institute.

Results

Our models are able to predict a future diagnosis of depression up to 12 months in advance (area under the receiver operating characteristic curve (AUC) 0.70–0.80). We can differentiate patients with severe baseline depression from those with minimal or mild baseline depression (AUC 0.72). Baseline depression severity was the strongest predictor of treatment response for medication and psychotherapy.

Conclusions

It is possible to use EHR data to predict a diagnosis of depression up to 12 months in advance and to differentiate between extreme baseline levels of depression. The models use commonly available data on diagnosis, medication, and clinical progress notes, making them easily portable. The ability to automatically determine severity can facilitate assembly of large patient cohorts with similar severity from multiple sites, which may enable elucidation of the moderators of treatment response in the future.     

Automated identification of drug and food allergies entered using non-standard terminology

Objective

An accurate computable representation of food and drug allergy is essential for safe healthcare. Our goal was to develop a high-performance, easily maintained algorithm to identify medication and food allergies and sensitivities from unstructured allergy entries in electronic health record (EHR) systems.

Materials and methods

An algorithm was developed in Transact-SQL to identify ingredients to which patients had allergies in a perioperative information management system. The algorithm used RxNorm and natural language processing techniques developed on a training set of 24 599 entries from 9445 records. Accuracy, specificity, precision, recall, and F-measure were determined for the training dataset and repeated for the testing dataset (24 857 entries from 9430 records).

Results

Accuracy, precision, recall, and F-measure for medication allergy matches were all above 98% in the training dataset and above 97% in the testing dataset for all allergy entries. Corresponding values for food allergy matches were above 97% and above 93%, respectively. Specificities of the algorithm were 90.3% and 85.0% for drug matches and 100% and 88.9% for food matches in the training and testing datasets, respectively.

Discussion

The algorithm had high performance for identification of medication and food allergies. Maintenance is practical, as updates are managed through upload of new RxNorm versions and additions to companion database tables. However, direct entry of codified allergy information by providers (through autocompleters or drop lists) is still preferred to post-hoc encoding of the data. Data tables used in the algorithm are available for download.

Conclusions

A high performing, easily maintained algorithm can successfully identify medication and food allergies from free text entries in EHR systems.     

Evaluation of an Android-based mHealth system for population surveillance in developing countries

Objective

In parts of the developing world traditionally modeled healthcare systems do not adequately meet the needs of the populace. This can be due to imbalances in both supply and demand—there may be a lack of sufficient healthcare and the population most at need may be unable or unwilling to take advantage of it. Home-based care has emerged as a possible mechanism to bring healthcare to the populace in a cost-effective, useful manner. This study describes the development, implementation, and evaluation of a mobile device-based system to support such services.

Materials and Methods

Mobile phones were utilized and a structured survey was implemented to be administered by community health workers using Open Data Kit. This system was used to support screening efforts for a population of two million persons in western Kenya.

Results

Users of the system felt it was easy to use and facilitated their work. The system was also more cost effective than pen and paper alternatives.

Discussion

This implementation is one of the largest applications of a system utilizing handheld devices for performing clinical care during home visits in a resource-constrained environment. Because the data were immediately available electronically, initial reports could be performed and important trends in data could thus be detected. This allowed adjustments to the programme to be made sooner than might have otherwise been possible.

Conclusion

A viable, cost-effective solution at scale has been developed and implemented for collecting electronic data during household visits in a resource-constrained setting.     

The impact of an electronic health record on nurse sensitive patient outcomes: an interrupted time series analysis

Objectives

To evaluate the impact of electronic health record (EHR) implementation on nursing care processes and outcomes.

Design

Interrupted time series analysis, 2003–2009.

Setting

A large US not-for-profit integrated health care organization.

Participants

29 hospitals in Northern and Southern California.

Intervention

An integrated EHR including computerized physician order entry, nursing documentation, risk assessment tools, and documentation tools.

Main outcome measures

Percentage of patients with completed risk assessments for hospital acquired pressure ulcers (HAPUs) and falls (process measures) and rates of HAPU and falls (outcome measures).

Results

EHR implementation was significantly associated with an increase in documentation rates for HAPU risk (coefficient 2.21, 95% CI 0.67 to 3.75); the increase for fall risk was not statistically significant (0.36; −3.58 to 4.30). EHR implementation was associated with a 13% decrease in HAPU rates (coefficient −0.76, 95% CI −1.37 to −0.16) but no decrease in fall rates (−0.091; −0.29 to 0.11). Irrespective of EHR implementation, HAPU rates decreased significantly over time (−0.16; −0.20 to −0.13), while fall rates did not (0.0052; −0.01 to 0.02). Hospital region was a significant predictor of variation for both HAPU (0.72; 0.30 to 1.14) and fall rates (0.57; 0.41 to 0.72).

Conclusions

The introduction of an integrated EHR was associated with a reduction in the number of HAPUs but not in patient fall rates. Other factors, such as changes over time and hospital region, were also associated with variation in outcomes. The findings suggest that EHR impact on nursing care processes and outcomes is dependent on a number of factors that should be further explored.     

Enrollment into a time sensitive clinical study in the critical care setting: results from computerized septic shock sniffer implementation

Objective

Recruitment of patients into time sensitive clinical trials in intensive care units (ICU) poses a significant challenge. Enrollment is limited by delayed recognition and late notification of research personnel. The objective of the present study was to evaluate the effectiveness of the implementation of electronic screening (septic shock sniffer) regarding enrollment into a time sensitive (24 h after onset) clinical study of echocardiography in severe sepsis and septic shock.

Design

We developed and tested a near-real time computerized alert system, the septic shock sniffer, based on established severe sepsis/septic shock diagnostic criteria. A sniffer scanned patients'' data in the electronic medical records and notified the research coordinator on call through an institutional paging system of potentially eligible patients.

Measurement

The performance of the septic shock sniffer was assessed.

Results

The septic shock sniffer performed well with a positive predictive value of 34%. Electronic screening doubled enrollment, with 68 of 4460 ICU admissions enrolled during the 9 months after implementation versus 37 of 4149 ICU admissions before sniffer implementation (p<0.05). Efficiency was limited by study coordinator availability (not available at nights or weekends).

Conclusions

Automated electronic medical records screening improves the efficiency of enrollment and should be a routine tool for the recruitment of patients into time sensitive clinical trials in the ICU setting.     

Key principles for a national clinical decision support knowledge sharing framework: synthesis of insights from leading subject matter experts

Objective

To identify key principles for establishing a national clinical decision support (CDS) knowledge sharing framework.

Materials and methods

As part of an initiative by the US Office of the National Coordinator for Health IT (ONC) to establish a framework for national CDS knowledge sharing, key stakeholders were identified. Stakeholders'' viewpoints were obtained through surveys and in-depth interviews, and findings and relevant insights were summarized. Based on these insights, key principles were formulated for establishing a national CDS knowledge sharing framework.

Results

Nineteen key stakeholders were recruited, including six executives from electronic health record system vendors, seven executives from knowledge content producers, three executives from healthcare provider organizations, and three additional experts in clinical informatics. Based on these stakeholders'' insights, five key principles were identified for effectively sharing CDS knowledge nationally. These principles are (1) prioritize and support the creation and maintenance of a national CDS knowledge sharing framework; (2) facilitate the development of high-value content and tooling, preferably in an open-source manner; (3) accelerate the development or licensing of required, pragmatic standards; (4) acknowledge and address medicolegal liability concerns; and (5) establish a self-sustaining business model.

Discussion

Based on the principles identified, a roadmap for national CDS knowledge sharing was developed through the ONC''s Advancing CDS initiative.

Conclusion

The study findings may serve as a useful guide for ongoing activities by the ONC and others to establish a national framework for sharing CDS knowledge and improving clinical care.     

Research and applications: A question of trust: user-centered design requirements for an informatics intervention to promote the sexual health of African-American youth

Objective

We investigated the user requirements of African-American youth (aged 14–24 years) to inform the design of a culturally appropriate, network-based informatics intervention for the prevention of HIV and other sexually transmitted infections (STI).

Materials and Methods

We conducted 10 focus groups with 75 African-American youth from a city with high HIV/STI prevalence. Data analyses involved coding using qualitative content analysis procedures and memo writing.

Results

Unexpectedly, the majority of participants’ design recommendations concerned trust. Youth expressed distrust towards people and groups, which was amplified within the context of information technology-mediated interactions about HIV/STI. Participants expressed distrust in the reliability of condoms and the accuracy of HIV tests. They questioned the benevolence of many institutions, and some rejected authoritative HIV/STI information. Therefore, reputational information, including rumor, influenced HIV/STI-related decision making. Participants’ design requirements also focused on trust-related concerns. Accordingly, we developed a novel trust-centered design framework to guide intervention design.

Discussion

Current approaches to online trust for health informatics do not consider group-level trusting patterns. Yet, trust was the central intervention-relevant issue among African-American youth, suggesting an important focus for culturally informed design. Our design framework incorporates: intervention objectives (eg, network embeddedness, participation); functional specifications (eg, decision support, collective action, credible question and answer services); and interaction design (eg, member control, offline network linkages, optional anonymity).

Conclusions

Trust is a critical focus for HIV/STI informatics interventions for young African Americans. Our design framework offers practical, culturally relevant, and systematic guidance to designers to reach this underserved group better.     

Targeted screening for pediatric conditions with the CHICA system

Objective

The Child Health Improvement through Computer Automation (CHICA) system is a decision-support and electronic-medical-record system for pediatric health maintenance and disease management. The purpose of this study was to explore CHICA''s ability to screen patients for disorders that have validated screening criteria—specifically tuberculosis (TB) and iron-deficiency anemia.

Design

Children between 0 and 11 years were randomized by the CHICA system. In the intervention group, parents were asked about TB and iron-deficiency risk, and physicians received a tailored prompt. In the control group, no screens were performed, and the physician received a generic prompt about these disorders.

Results

1123 participants were randomized to the control group and 1116 participants to the intervention group. Significantly more people reported positive risk factors for iron-deficiency anemia in the intervention group (17.5% vs 3.1%, OR 6.6, 95% CI 4.5 to 9.5). In general, far fewer parents reported risk factors for TB than for iron-deficiency anemia. Again, there were significantly higher detection rates of positive risk factors in the intervention group (1.8% vs 0.8%, OR 2.3, 95% CI 1.0 to 5.0).

Limitations

It is possible that there may be more positive screens without improving outcomes. However, the guidelines are based on studies that have evaluated the questions the authors used as sensitive and specific, and there is no reason to believe that parents misunderstood them.

Conclusions

Many screening tests are risk-based, not universal, leaving physicians to determine who should have a further workup. This can be a time-consuming process. The authors demonstrated that the CHICA system performs well in assessing risk automatically for TB and iron-deficiency anemia.     

The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data

Objective

Predicting patient outcomes from genome-wide measurements holds significant promise for improving clinical care. The large number of measurements (eg, single nucleotide polymorphisms (SNPs)), however, makes this task computationally challenging. This paper evaluates the performance of an algorithm that predicts patient outcomes from genome-wide data by efficiently model averaging over an exponential number of naive Bayes (NB) models.

Design

This model-averaged naive Bayes (MANB) method was applied to predict late onset Alzheimer''s disease in 1411 individuals who each had 312 318 SNP measurements available as genome-wide predictive features. Its performance was compared to that of a naive Bayes algorithm without feature selection (NB) and with feature selection (FSNB).

Measurement

Performance of each algorithm was measured in terms of area under the ROC curve (AUC), calibration, and run time.

Results

The training time of MANB (16.1 s) was fast like NB (15.6 s), while FSNB (1684.2 s) was considerably slower. Each of the three algorithms required less than 0.1 s to predict the outcome of a test case. MANB had an AUC of 0.72, which is significantly better than the AUC of 0.59 by NB (p<0.00001), but not significantly different from the AUC of 0.71 by FSNB. MANB was better calibrated than NB, and FSNB was even better in calibration. A limitation was that only one dataset and two comparison algorithms were included in this study.

Conclusion

MANB performed comparatively well in predicting a clinical outcome from a high-dimensional genome-wide dataset. These results provide support for including MANB in the methods used to predict outcomes from large, genome-wide datasets.     

Temporal trends of hemoglobin A1c testing

Objective

The study of utilization patterns can quantify potential overuse of laboratory tests and find new ways to reduce healthcare costs. We demonstrate the use of distributional analytics for comparing electronic health record (EHR) laboratory test orders across time to diagnose and quantify overutilization.

Materials and methods

We looked at hemoglobin A1c (HbA1c) testing across 119 000 patients and 15 years of hospital records. We examined the patterns of HbA1c ordering before and after the publication of the 2002 American Diabetes Association guidelines for HbA1c testing. We conducted analyses to answer three questions. What are the patterns of HbA1c ordering? Do HbA1c orders follow the guidelines with respect to frequency of measurement? If not, how and why do they depart from the guidelines?

Results

The raw number of HbA1c orderings has steadily increased over time, with a specific increase in low-measurement orderings (<6.5%). There is a change in ordering pattern following the 2002 guideline (p<0.001). However, by comparing ordering distributions, we found that the changes do not reflect the guidelines and rather exhibit a new practice of rapid-repeat testing. The rapid-retesting phenomenon does not follow the 2009 guidelines for diabetes diagnosis either, illustrated by a stratified HbA1c value analysis.

Discussion

Results suggest HbA1c test overutilization, and contributing factors include lack of care coordination, unexpected values prompting retesting, and point-of-care tests followed by confirmatory laboratory tests.

Conclusions

We present a method of comparing ordering distributions in an EHR across time as a useful diagnostic approach for identifying and assessing the trend of inappropriate use over time.     

Understanding differences in electronic health record (EHR) use: linking individual physicians’ perceptions of uncertainty and EHR use patterns in ambulatory care

Objective

Electronic health records (EHR) hold great promise for managing patient information in ways that improve healthcare delivery. Physicians differ, however, in their use of this health information technology (IT), and these differences are not well understood. The authors study the differences in individual physicians'' EHR use patterns and identify perceptions of uncertainty as an important new variable in understanding EHR use.

Design

Qualitative study using semi-structured interviews and direct observation of physicians (n=28) working in a multispecialty outpatient care organization.

Measurements

We identified physicians'' perceptions of uncertainty as an important variable in understanding differences in EHR use patterns. Drawing on theories from the medical and organizational literatures, we identified three categories of perceptions of uncertainty: reduction, absorption, and hybrid. We used an existing model of EHR use to categorize physician EHR use patterns as high, medium, and low based on degree of feature use, level of EHR-enabled communication, and frequency that EHR use patterns change.

Results

Physicians'' perceptions of uncertainty were distinctly associated with their EHR use patterns. Uncertainty reductionists tended to exhibit high levels of EHR use, uncertainty absorbers tended to exhibit low levels of EHR use, and physicians demonstrating both perspectives of uncertainty (hybrids) tended to exhibit medium levels of EHR use.

Conclusions

We find evidence linking physicians'' perceptions of uncertainty with EHR use patterns. Study findings have implications for health IT research, practice, and policy, particularly in terms of impacting health IT design and implementation efforts in ways that consider differences in physicians'' perceptions of uncertainty.     

An analysis of electronic health record-related patient safety concerns

Objective

A recent Institute of Medicine report called for attention to safety issues related to electronic health records (EHRs). We analyzed EHR-related safety concerns reported within a large, integrated healthcare system.

Methods

The Informatics Patient Safety Office of the Veterans Health Administration (VA) maintains a non-punitive, voluntary reporting system to collect and investigate EHR-related safety concerns (ie, adverse events, potential events, and near misses). We analyzed completed investigations using an eight-dimension sociotechnical conceptual model that accounted for both technical and non-technical dimensions of safety. Using the framework analysis approach to qualitative data, we identified emergent and recurring safety concerns common to multiple reports.

Results

We extracted 100 consecutive, unique, closed investigations between August 2009 and May 2013 from 344 reported incidents. Seventy-four involved unsafe technology and 25 involved unsafe use of technology. A majority (70%) involved two or more model dimensions. Most often, non-technical dimensions such as workflow, policies, and personnel interacted in a complex fashion with technical dimensions such as software/hardware, content, and user interface to produce safety concerns. Most (94%) safety concerns related to either unmet data-display needs in the EHR (ie, displayed information available to the end user failed to reduce uncertainty or led to increased potential for patient harm), software upgrades or modifications, data transmission between components of the EHR, or ‘hidden dependencies’ within the EHR.

Discussion

EHR-related safety concerns involving both unsafe technology and unsafe use of technology persist long after ‘go-live’ and despite the sophisticated EHR infrastructure represented in our data source. Currently, few healthcare institutions have reporting and analysis capabilities similar to the VA.

Conclusions

Because EHR-related safety concerns have complex sociotechnical origins, institutions with long-standing as well as recent EHR implementations should build a robust infrastructure to monitor and learn from them.