Decreased Rates of Inpatient Hypoglycemia Following Implementation of an Automated Tool in the Electronic Medical Record for Identifying Root Causes.

OBJECTIVE: The objective was to identify root causes of hypoglycemia on medicine inpatient units using an automated tool. Data collected will guide educational interventions aimed at improving patient care and safety by decreasing rates of hypoglycemia. METHODS: A survey was conducted among RNs to identify risk factors for hypoglycemia. Survey data were used to create a hypoglycemia root cause survey tool in the EMR. RNs were prompted to utilize the tool when blood glucose (BG) < 70 mg/dL. Once the most common modifiable cause of hypoglycemia was identified, an educational intervention for safe and effective use of insulin was launched. This strategy was designed to empower the care team to reduce the insulin dose when appropriate to prevent future hypoglycemic episodes. RESULTS: BG data were compared from March and April in 2016 and 2017. Rates of hypoglycemia (BG < 70 mg/dL) decreased from 2.3% to 1.5%; BG values in target range (70-180 mg/dL) increased from 59.4% to 65.7%; hyperglycemia (BG > 180 mg/dL) decreased from 38.3% to 32.8% (all P values < .001). The number of patients with recurrent hypoglycemia (3 or more episodes) decreased from 5.7% to 2.2% ( P = .044). CONCLUSIONS: The two most frequent modifiable causes of hypoglycemia (insulin and nutrition) were identified by an RN survey and confirmed by chart review. A targeted educational intervention addressing safe and effective insulin dosing resulted in a significant decrease in both hypoglycemia and recurrent hypoglycemia. This was associated with an improvement in overall glycemic control. Ongoing clinician education regarding insulin and nutrition accompanied by discussions between RNs and prescribers to address hypoglycemic events in real-time could continue to lower the rate of occurrence.

Designing a clinical dashboard to fill information gaps in the emergency department.

Data fragmentation within electronic health records causes gaps in the information readily available to clinicians. We investigated the information needs of emergency medicine clinicians in order to design an electronic dashboard to fill information gaps in the emergency department. An online survey was distributed to all emergency medicine physicians at a large, urban academic medical center. The survey response rate was 48% (52/109). The clinical information items reported to be most helpful while caring for patients in the emergency department were vital signs, electrocardiogram (ECG) reports, previous discharge summaries, and previous lab results. Brief structured interviews were also conducted with 18 clinicians during their shifts in the emergency department. From the interviews, three themes emerged: 1) difficulty accessing vital signs, 2) difficulty accessing point-of-care tests, and 3) difficulty comparing the current ECG with the previous ECG. An emergency medicine clinical dashboard was developed to address these difficulties.

Enabling claims-based decision support through non-interruptive capture of admission diagnoses and provider billing codes.

The patient problem list, like administrative claims data, has become an important source of data for decision support, patient cohort identification, and alerting systems. A two-fold intervention to increase capture of problems on the problem list automatically - with minimal disruption to admitting and provider billing workflows - is described. For new patients with no prior data in the electronic health record, the intervention resulted in a statistically significant increase in the number of problems recorded to the problem list (3.8 vs 2.9 problems post-and pre-intervention respectively, p value 2×10(-16)). The majority of problems were recorded in the first 24 hours of admission. The proportion of patients with at least one problem coded to the problem list within the first 24 hours increased from 94% to 98% before and after intervention (chi square 344, p value 2×10(-16)). ICD9 "V codes" connoting circumstances beyond disease were captured at a higher rate post intervention than before. Deyo/Charlson comorbidities derived from problem list data were more similar to those derived from claims data after the intervention than before (Jaccard similarity 0.3 post- vs 0.21 pre-intervention, p value 2×10(-16)). A workflow-sensitive, non-interruptive means of capturing provider-entered codes early in admission can improve both the quantity and content of problems on the patient problem list.

Implementation of a computerized patient handoff application.

For hospitalized patients, handoffs between providers affect continuity of care and increase the risk of medical errors. Most commercial electronic health record (EHR) systems lack dedicated tools to support patient handoff activities. We developed a collaborative application supporting patient handoff that is fully integrated with our commercial EHR. The application creates user-customizable printed reports with automatic inclusion of a variety of EHR data, including: allergies, medications, 24-hour vital signs, recent common laboratory test results, isolation requirements, and code status. It has achieved widespread voluntary use at our institution (6,100 monthly users; 700 daily reports generated), and we have distributed the application to several other institutions using the same EHR. Though originally designed for resident physicians, today about 50% of the application users are nurses, 40% are physicians/physician assistants/nurse practitioners, and 10% are pharmacists, social workers, and other allied health providers.

Use of electronic clinical documentation: time spent and team interactions.

OBJECTIVE: To measure the time spent authoring and viewing documentation and to study patterns of usage in healthcare practice. DESIGN: Audit logs for an electronic health record were used to calculate rates, and social network analysis was applied to ascertain usage patterns. Subjects comprised all care providers at an urban academic medical center who authored or viewed electronic documentation. MEASUREMENT: Rate and time of authoring and viewing clinical documentation, and associations among users were measured. RESULTS: Users spent 20-103 min per day authoring notes and 7-56 min per day viewing notes, with physicians spending less than 90 min per day total. About 16% of attendings' notes, 8% of residents' notes, and 38% of nurses' notes went unread by other users, and, overall, 16% of notes were never read by anyone. Viewing of notes dropped quickly with the age of the note, but notes were read at a low but measurable rate, even after 2 years. Most healthcare teams (77%) included a nurse, an attending, and a resident, and those three users' groups were the first to write notes during an admission. Limitations The limitations were restriction to a single academic medical center and use of log files without direct observation. CONCLUSIONS: Care providers spend a significant amount of time viewing and authoring notes. Many notes are never read, and rates of usage vary significantly by author and viewer. While the rate of viewing a note drops quickly with its age, even after 2 years inpatient notes are still viewed.

Awareness of the Care Team in Electronic Health Records.

OBJECTIVE: To support collaboration and clinician-targeted decision support, electronic health records (EHRs) must contain accurate information about patients' care providers. The objective of this study was to evaluate two approaches for care provider identification employed within a commercial EHR at a large academic medical center. METHODS: We performed a retrospective review of EHR data for 121 patients in two cardiology wards during a four-week period. System audit logs of chart accesses were analyzed to identify the clinicians who were likely participating in the patients' hospital care. The audit log data were compared with two functions in the EHR for documenting care team membership: 1) a vendor-supplied module called "Care Providers", and 2) a custom "Designate Provider" order that was created primarily to improve accuracy of the attending physician of record documentation. RESULTS: For patients with a 3-5 day hospital stay, an average of 30.8 clinicians accessed the electronic chart, including 10.2 nurses, 1.4 attending physicians, 2.3 residents, and 5.4 physician assistants. The Care Providers module identified 2.7 clinicians/patient (1.8 attending physicians and 0.9 nurses). The Designate Provider order identified 2.1 clinicians/patient (1.1 attending physicians, 0.2 resident physicians, and 0.8 physician assistants). Information about other members of patients' care teams (social workers, dietitians, pharmacists, etc.) was absent. CONCLUSIONS: The two methods for specifying care team information failed to identify numerous individuals involved in patients' care, suggesting that commercial EHRs may not provide adequate tools for care team designation. Improvements to EHR tools could foster greater collaboration among care teams and reduce communication-related risks to patient safety.