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Concept: Electronic medical record

171

Purchasing electronic health records (EHRs) typically follows a process in which potential adopters actively seek information, compare alternatives, and form attitudes towards the product. A potential source of information on EHRs that can be used in the process is vendor websites. It is unclear how much product information is presented on EHR vendor websites or the extent of its value during EHR purchasing decisions.

Concepts: Electronic health record, Electronic medical record, Medical record, Health informatics, Medical informatics, Personal health record, Gramophone record, Public records

110

Methods to identify and study safety risks of electronic health records (EHRs) are underdeveloped and largely depend on limited end-user reports. “Safety huddles” have been found useful in creating a sense of collective situational awareness that increases an organization’s capacity to respond to safety concerns. We explored the use of safety huddles for identifying and learning about EHR-related safety concerns.

Concepts: Electronic health record, Electronic medical record, Health informatics, Medical informatics, Personal health record

54

Most electronic health record systems provide laboratory test results to patients in table format. We tested whether presenting such results in visual displays (number lines) could improve understanding.

Concepts: Electronic health record, Electronic medical record, Health informatics, Andrew Martin, Medical informatics, Personal health record

44

 To assess the short term association of inpatient implementation of electronic health records (EHRs) with patient outcomes of mortality, readmissions, and adverse safety events.

Concepts: Electronic health record, Electronic medical record, Health informatics, Medical informatics, Personal health record

39

Insomnia remains under-diagnosed and poorly treated despite its high economic and social costs. Though previous work has examined how patient characteristics affect sleep medication prescriptions, the role of physician characteristics that influence this clinical decision remains unclear. We sought to understand patient and physician factors that influence sleep medication prescribing patterns by analyzing Electronic Medical Records (EMRs) including the narrative clinical notes as well as codified data. Zolpidem and trazodone were the most widely prescribed initial sleep medication in a cohort of 1,105 patients. Some providers showed a historical preference for one medication, which was highly predictive of their future prescribing behavior. Using a predictive model (AUC = 0.77), physician preference largely determined which medication a patient received (OR = 3.13; p = 3 × 10(-37)). In addition to the dominant effect of empirically determined physician preference, discussion of depression in a patient’s note was found to have a statistically significant association with receiving a prescription for trazodone (OR = 1.38, p = 0.04). EMR data can yield insights into physician prescribing behavior based on real-world physician-patient interactions.

Concepts: Scientific method, Prescription drug, Medicine, Patient, Physician, Electronic medical record, Medical prescription, Insomnia

35

Health care providers remain uncertain about how they will fare financially if they adopt electronic health record (EHR) systems. We used survey data from forty-nine community practices in a large EHR pilot, the Massachusetts eHealth Collaborative, to project five-year returns on investment. We found that the average physician would lose $43,743 over five years; just 27 percent of practices would have achieved a positive return on investment; and only an additional 14 percent of practices would have come out ahead had they received the $44,000 federal meaningful-use incentive. The largest difference between practices with a positive return on investment and those with a negative return was the extent to which they used their EHRs to increase revenue, primarily by seeing more patients per day or by improved billing that resulted in fewer rejected claims and more accurate coding. Almost half of the practices did not realize savings in paper medical records because they continued to keep records on paper. We conclude that current meaningful use incentives alone may not ensure that most practices, particularly smaller ones, achieve a positive return on investment from EHR adoption. Policies that provide additional support, such as expanding the regional extension center program, could help ensure that practices make the changes required to realize a positive return on investment from EHRs.

Concepts: Investment, Electronic health record, Electronic medical record, Medical record, Health informatics, Medical informatics, Rate of return, Internal rate of return

34

Achieving nationwide adoption of electronic health records (EHRs) remains an important policy priority. While EHR adoption has increased steadily since 2010, it is unclear how providers that have not yet adopted will fare now that federal incentives have converted to penalties. We used 2008-14 national data, which includes the most recently available, to examine hospital EHR trends. We found large gains in adoption, with 75 percent of US hospitals now having adopted at least a basic EHR system-up from 59 percent in 2013. However, small and rural hospitals continue to lag behind. Among hospitals without a basic EHR system, the function most often not yet adopted (in 61 percent of hospitals) was physician notes. We also saw large increases in the ability to meet core stage 2 meaningful-use criteria (40.5 percent of hospitals, up from 5.8 percent in 2013); much of this progress resulted from increased ability to meet criteria related to exchange of health information with patients and with other physicians during care transitions. Finally, hospitals most often reported up-front and ongoing costs, physician cooperation, and complexity of meeting meaningful-use criteria as challenges. Our findings suggest that nationwide hospital EHR adoption is in reach but will require attention to small and rural hospitals and strategies to address financial challenges, particularly now that penalties for lack of adoption have begun.

Concepts: Patient, Hospital, Physician, Electronic health record, Electronic medical record, Health informatics, Medical informatics, Personal health record

34

In response to mounting evidence that use of electronic medical record systems may cause unintended consequences, and even patient harm, the AMIA Board of Directors convened a Task Force on Usability to examine evidence from the literature and make recommendations. This task force was composed of representatives from both academic settings and vendors of electronic health record (EHR) systems. After a careful review of the literature and of vendor experiences with EHR design and implementation, the task force developed 10 recommendations in four areas: (1) human factors health information technology (IT) research, (2) health IT policy, (3) industry recommendations, and (4) recommendations for the clinician end-user of EHR software. These AMIA recommendations are intended to stimulate informed debate, provide a plan to increase understanding of the impact of usability on the effective use of health IT, and lead to safer and higher quality care with the adoption of useful and usable EHR systems.

Concepts: Electronic health record, Electronic medical record, Health informatics, Medical informatics, VistA, Computer physician order entry, Usability, Personal health record

30

Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers (IL-6, nCD64, IL-1ra, PCT, MCP1, and G-CSF) yielded the same predictive power as collecting an additional 16 hours of EMR data(AUC of 0.80), suggesting that the biomarkers may be useful for identifying these patients earlier. Ultimately, supervised learning using a subset of biomarker and EMR data as features may be capable of identifying patients in the early to peak phase of sepsis in a diverse population and may provide a tool for more timely identification and intervention.

Concepts: Hospital, Machine learning, Receiver operating characteristic, Electronic medical record, Identification, Supervised learning

29

Adoption of electronic health record (EHR) systems has increased significantly across the nation. Whether EHR use has translated into improved quality of care and outcomes in heart failure (HF) is not well studied.

Concepts: Health care, Electronic health record, Electronic medical record, The Nation, Personal health record