Neonatal Informatics: Optimizing Clinical Data Entry and Display

HistoriView: Implementation and Evaluation of a Novel Approach to Review a Patient Using a Scalable Space-Efficient Timeline without Zoom Interactions

BACKGROUND

 Timelines have been used for patient review. While maintaining a compact overview is important, merged event representations caused by the intricate and voluminous patient data bring event recognition, access ambiguity, and inefficient interaction problems. Handling large patient data efficiently is another challenge.

OBJECTIVE

 This study aims to develop a scalable, efficient timeline to enhance patient review for research purposes. The focus is on addressing the challenges presented by the intricate and voluminous patient data.

METHODS

 We propose a high-throughput, space-efficient HistoriView timeline for an individual patient. For a compact overview, it uses nonstacking event representation. An overlay detection algorithm, y-shift visualization, and popup-based interaction facilitate comprehensive analysis of overlapping datasets. An i2b2 HistoriView plugin was deployed, using split query and event reduction approaches, delivering the entire history efficiently without losing information. For evaluation, 11 participants completed a usability survey and a preference survey, followed by qualitative feedback. To evaluate scalability, 100 randomly selected patients over 60 years old were tested on the plugin and were compared with a baseline visualization.

RESULTS

 Most participants found that HistoriView was easy to use and learn and delivered information clearly without zooming. All preferred HistoriView over a stacked timeline. They expressed satisfaction on display, ease of learning and use, and efficiency. However, challenges and suggestions for improvement were also identified. In the performance test, the largest patient had 32,630 records, which exceeds the baseline limit. HistoriView reduced it to 2,019 visual artifacts. All patients were pulled and visualized within 45.40 seconds. Visualization took less than 3 seconds for all.

DISCUSSION AND CONCLUSION

 HistoriView allows complete data exploration without exhaustive interactions in a compact overview. It is useful for dense data or iterative comparisons. However, issues in exploring subconcept records were reported. HistoriView handles large patient data preserving original information in a reasonable time.

Usability of human-computer interaction in neonatal care

While a goal for Electronic Health Record (EHR) technologies was to improve quality, efficiency, and safety, the usability of EHRs has remained poor. The relation to patient harm and user satisfaction cannot be ignored. Optimization of EHR usability is imperative to improving the outcomes for critically ill patients, especially neonates who are at the extremes of physiologic variability. Further development and integration of metadata with predictive modeling and clinical protocols can support provider decision making, increase efficiency and safety, and reduce clinician burnout. This paper reviews EHR usability and identifies opportunities to improve the EHR specific to neonatal care.

Neurological Dashboards and Consultation Turnaround Time at an Academic Medical Center

BACKGROUND

Neurologists perform a significant amount of consultative work. Aggregative electronic health record (EHR) dashboards may help to reduce consultation turnaround time (TAT) which may reflect time spent interfacing with the EHR.

OBJECTIVES

This study was aimed to measure the difference in TAT before and after the implementation of a neurological dashboard.

METHODS

We retrospectively studied a neurological dashboard in a read-only, web-based, clinical data review platform at an academic medical center that was separate from our institutional EHR. Using our EHR, we identified all distinct initial neurological consultations at our institution that were completed in the 5 months before, 5 months after, and 12 months after the dashboard go-live in December 2017. Using log data, we determined total dashboard users, unique page hits, patient-chart accesses, and user departments at 5 months after go-live. We calculated TAT as the difference in time between the placement of the consultation order and completion of the consultation note in the EHR.

RESULTS

By April 30th in 2018, we identified 269 unique users, 684 dashboard page hits (median hits/user 1.0, interquartile range [IQR] = 1.0), and 510 unique patient-chart accesses. In 5 months before the go-live, 1,434 neurology consultations were completed with a median TAT of 2.0 hours (IQR = 2.5) which was significantly longer than during 5 months after the go-live, with 1,672 neurology consultations completed with a median TAT of 1.8 hours (IQR = 2.2; p = 0.001). Over the following 7 months, 2,160 consultations were completed and median TAT remained unchanged at 1.8 hours (IQR = 2.5).

CONCLUSION

At a large academic institution, we found a significant decrease in inpatient consult TAT 5 and 12 months after the implementation of a neurological dashboard. Further study is necessary to investigate the cognitive and operational effects of aggregative dashboards in neurology and to optimize their use.

The Impact of Implementation of a Clinically Integrated Problem-Based Neonatal Electronic Health Record on Documentation Metrics, Provider Satisfaction, and Hospital Reimbursement: A Quality Improvement Project

Background

A goal of effective electronic health record provider documentation platforms is to provide an efficient, concise, and comprehensive notation system that will effectively reflect the clinical course, including the diagnoses, treatments, and interventions.

Objective

The aim is to fully redesign and standardize the provider documentation process, seeking improvement in documentation based on ongoing All Patient Refined Diagnosis Related Group–based coding records, while maintaining noninferiority comparing provider satisfaction to our existing documentation process. We estimated the fiscal impact of improved documentation based on changes in expected hospital payments.

Methods

Employing a multidisciplinary collaborative approach, we created an integrated clinical platform that captures data entry from the obstetrical suite, delivery room, neonatal intensive care unit (NICU) nursing and respiratory therapy staff. It provided the sole source for hospital provider documentation in the form of a history and physical exam, daily progress notes, and discharge summary. Health maintenance information, follow-up appointments, and running contemporaneous updated hospital course information have selected shared entry and common viewing by the NICU team. The interventions were to (1) improve provider awareness of appropriate documentation through a provider education handout and follow-up group discussion and (2) fully redesign and standardize the provider documentation process building from the native Epic-based software. The measures were (1) hospital coding department review of all NICU admissions and 3M All Patient Refined Diagnosis Related Group–based calculations of severity of illness, risk of mortality, and case mix index scores; (2) balancing measure: provider time utilization case study and survey; and (3) average expected hospital payment based on acuity-based clinical logic algorithm and payer mix.

Results

We compared preintervention (October 2015-October 2016) to postintervention (November 2016-May 2017) time periods and saw: (1) significant improvement in All Patient Refined Diagnosis Related Group–derived severity of illness, risk of mortality, and case mix index (monthly average severity of illness scores increased by 11.1%, P=.008; monthly average risk of mortality scores increased by 13.5%, P=.007; and monthly average case mix index scores increased by 7.7%, P=.009); (2) time study showed increased time to complete history and physical and progress notes and decreased time to complete discharge summary (history and physical exam: time allocation increased by 47%, P=.05; progress note: time allocation increased by 91%, P<.001; discharge summary: time allocation decreased by 41%, P=.03); (3) survey of all providers: overall there was positive provider perception of the new documentation process based on a survey of the provider group; (4) significantly increased hospital average expected payments: comparing the preintervention and postintervention study periods, there was a US $14,020 per month per patient increase in average expected payment for hospital charges (P<.001). There was no difference in payer mix during this time period.

Conclusions

A problem-based NICU documentation electronic health record more effectively improves documentation without dissatisfaction by the participating providers and improves hospital estimations of All Patient Refined Diagnosis Related Group–based revenue.

Clinical data needs in the neonatal intensive care unit electronic medical record

Background

The amount of clinical information that providers encounter daily creates an environment for information overload and medical error. To create a more efficient EMR human-computer interface, we aimed to understand clinical information needs among NICU providers.

Methods

A web-based survey to evaluate 98 data items was created and distributed to NICU providers. Participants were asked to rate the importance of each data item in helping them make routine clinical decisions in the NICU.

Results

There were 23 responses (92% – response rate) with participants distributed among four clinical roles. The top 5 items with the highest mean score were daily weight, pH, pCO2, FiO2, and blood culture results. When compared by clinical role groupings, supervisory physicians gave individual data item ratings at the extremes of the scale when compared to providers more responsible for the daily clinical care of NICU patients.

Conclusion

NICU providers demonstrate a need for large amounts of EMR data to help guide clinical decision making with differences found when comparing by clinical role. When creating an EMR interface in the NICU there may be a need to offer options for varying degrees of viewable data densities depending on clinical role.

Clinical informatics sub-specialty board certification

Increased funding for health information technology and the advance of electronic health records in hospitals and practices have created the need for a new specialist: the clinical informatician. Clinical informatics was recognized in 2011 as the latest subspecialty in medicine by the American Board of Medical Specialties. This article reviews the need for this new specialty as well as the steps necessary for its creation. The content and training requirements for clinical informatics are discussed as well as eligibility criteria for taking the board examination. Training programs as well as board preparation are addressed along with the expected impact that this new field will have on the practice of medicine.