Information needs for the OR and PACU electronic medical record

Why do probabilistic clinical models fail to transport between sites

The rising popularity of artificial intelligence in healthcare is highlighting the problem that a computational model achieving super-human clinical performance at its training sites may perform substantially worse at new sites. In this perspective, we argue that we should typically expect this failure to transport, and we present common sources for it, divided into those under the control of the experimenter and those inherent to the clinical data-generating process. Of the inherent sources we look a little deeper into site-specific clinical practices that can affect the data distribution, and propose a potential solution intended to isolate the imprint of those practices on the data from the patterns of disease cause and effect that are the usual target of probabilistic clinical models.

Development and evaluation of an anesthesia module for electronic medical records in the operating room: an applied developmental study

Developing an anesthesia module in the operating room is one of the significant steps toward the implementation of electronic medical records (EMR) in health care centers. This study aimed to develop and evaluate the web based-anesthesia module of an electronic medical record Sciences, in the operating room of the Namazi Medical Training Center of Shiraz University of Medical Iran. This developmental and applied study was conducted in steps including determining the functional and non-functional requirements, designing and implementing the anesthesia module, and usability evaluation. 3 anesthesiologists, 3 anesthesiologist assistants, and 12 anesthetist nurses were included in the study as a research community. React.js, Node.js programming language to program this module, Mongo dB database, and Windows server for data management and USE standard questionnaire were used. In the anesthesia module, software quality features were determined as functional requirements and non-functional requirements included 286 data elements in 25 categories (demographic information, surgery information, laboratory results, patient graphs, consults, consent letter, physical examinations, medication history, family disease records, social record, past medical history, type of anesthesia, anesthesia induction method, airway management, monitoring, anesthesia chart, blood and fluids, blood gases, tourniquets and warmers, accessories, positions, neuromuscular reversal, transfer the patient from the operating room, complications of anesthesia and, seal/ signature). Also, after implementing the anesthesia module, results of the usability evaluation showed that 69.1% of the users agreed with the use of this module in the operating room and considered it user-friendly.

Information and Data Visualization Needs among Direct Care Nurses in the Intensive Care Unit

OBJECTIVES

Intensive care unit (ICU) direct care nurses spend 22% of their shift completing tasks within the electronic health record (EHR). Miscommunications and inefficiencies occur, particularly during patient hand-off, placing patient safety at risk. Redesigning how direct care nurses visualize and interact with patient information during hand-off is one opportunity to improve EHR use. A web-based survey was deployed to better understand the information and visualization needs at patient hand-off to inform redesign.

METHODS

A multicenter anonymous web-based survey of direct care ICU nurses was conducted (9-12/2021). Semi-structured interviews with stakeholders informed survey development. The primary outcome was identifying primary EHR data needs at patient hand-off for inclusion in future EHR visualization and interface development. Secondary outcomes included current use of the EHR at patient hand-off, EHR satisfaction, and visualization preferences. Frequencies, means, and medians were calculated for each data item then ranked in descending order to generate proportional quarters using SAS v9.4.

RESULTS

In total, 107 direct care ICU nurses completed the survey. The majority (46%, n = 49/107) use the EHR at patient hand-off to verify exchanged verbal information. Sixty-four percent (n = 68/107) indicated that current EHR visualization was insufficient. At the start of an ICU shift, primary EHR data needs included hemodynamics (mean 4.89 ± 0.37, 98%, n = 105), continuous IV medications (4.55 ± 0.73, 93%, n = 99), laboratory results (4.60 ± 0.56, 96%, n = 103), mechanical circulatory support devices (4.62 ± 0.72, 90%, n = 97), code status (4.40 ± 0.85, 59%, n = 108), and ventilation status (4.35 + 0.79, 51%, n = 108). Secondary outcomes included mean EHR satisfaction of 65 (0-100 scale, standard deviation = ± 21) and preferred future EHR user-interfaces to be organized by organ system (53%, n = 57/107) and visualized by tasks/schedule (61%, n = 65/107).

CONCLUSION

We identified information and visualization needs of direct care ICU nurses. The study findings could serve as a baseline toward redesigning an EHR interface.

Physician experiences of screen-level features in a prominent electronic health record: Design recommendations from a qualitative study

The goal of this qualitative study was to assess physicians' perceptions around features of key screens within a prominent commercial EHR, and to solicit end-user recommendations for improved retrieval of high-priority clinical information. We conducted a qualitative, descriptive study of 25 physicians in a medical ICU setting. at a tertiary academic medical center. An in-depth, semi-structured interview guide was developed to elicit physician perceptions on information retrieval as well as favorable and unfavorable features of specific EHR screens. Transcripts were independently coded in a qualitative software management tool by at least two trained coders using a common code book. We successfully obtained vendor permission to map physicians perception's on full Epic© screenshots. Among the 25 physician participants (13 female; 5 attending physicians, 9 fellows, 11 residents), the majority of participants reported experiencing challenges finding clinical information in the EHR. We present the most favorable and unfavorable screen-level features for four central EHR screens: Flowsheet, Notes/Chart Review, Results Review, and Vital Signs. We also compiled participants' recommendations for a comprehensive EHR dashboard screen to better support clinical workflow and information retrieval in the medical ICU through User-Centered Design. ICU physicians demonstrated a mix of positive and negative attitudes toward specific screen-level features in a major vendor-based EHR system. Physician perceptions of information overload emerged as a theme across multiple EHR screens. Our findings underscore the importance of qualitative research and end-user feedback in EHR software design and interface optimization at both the vendor and institutional level.

Ascertaining Design Requirements for Postoperative Care Transition Interventions

BACKGROUND

Handoffs or care transitions from the operating room (OR) to intensive care unit (ICU) are fragmented and vulnerable to communication errors. Although protocols and checklists for standardization help reduce errors, such interventions suffer from limited sustainability. An unexplored aspect is the potential role of developing personalized postoperative transition interventions using artificial intelligence (AI)-generated risks.

OBJECTIVES

This study was aimed to (1) identify factors affecting sustainability of handoff standardization, (2) utilize a human-centered approach to develop design ideas and prototyping requirements for a sustainable handoff intervention, and (3) explore the potential role for AI risk assessment during handoffs.

METHODS

We conducted four design workshops with 24 participants representing OR and ICU teams at a large medical academic center. Data collection phases were (1) open-ended questions, (2) closed card sorting of handoff information elements, and (3) scenario-based design ideation and prototyping for a handoff intervention. Data were analyzed using thematic analysis. Card sorts were further tallied to characterize handoff information elements as core, flexible, or unnecessary.

RESULTS

Limited protocol awareness among clinicians and lack of an interdisciplinary electronic health record (EHR)-integrated handoff intervention prevented long-term sustainability of handoff standardization. Clinicians argued for a handoff intervention comprised of core elements (included for all patients) and flexible elements (tailored by patient condition and risks). They also identified unnecessary elements that could be omitted during handoffs. Similarities and differences in handoff intervention requirements among physicians and nurses were noted; in particular, clinicians expressed divergent views on the role of AI-generated postoperative risks.

CONCLUSION

Current postoperative handoff interventions focus largely on standardization of information transfer and handoff processes. Our design approach allowed us to visualize accurate models of user expectations for effective interdisciplinary communication. Insights from this study point toward EHR-integrated, "flexibly standardized" care transition interventions that can automatically generate a patient-centered summary and risk-based report.

User-Centered Clinical Display Design Issues for Inpatient Providers

BACKGROUND

Suboptimal information display in electronic health records (EHRs) is a notorious pain point for users. Designing an effective display is difficult, due in part to the complex and varied nature of clinical practice.

OBJECTIVE

This article aims to understand the goals, constraints, frustrations, and mental models of inpatient medical providers when accessing EHR data, to better inform the display of clinical information.

METHODS

A multidisciplinary ethnographic study of inpatient medical providers.

RESULTS

Our participants' primary goal was usually to assemble a clinical picture around a given question, under the constraints of time pressure and incomplete information. To do so, they tend to use a mental model of multiple layers of abstraction when thinking of patients and disease; they prefer immediate pattern recognition strategies for answering clinical questions, with breadth-first or depth-first search strategies used subsequently if needed; and they are sensitive to data relevance, completeness, and reliability when reading a record.

CONCLUSION

These results conflict with the ubiquitous display design practice of separating data by type (test results, medications, notes, etc.), a mismatch that is known to encumber efficient mental processing by increasing both navigation burden and memory demands on users. A popular and obvious solution is to select or filter the data to display exactly what is presumed to be relevant to the clinical question, but this solution is both brittle and mistrusted by users. A less brittle approach that is more aligned with our users' mental model could use abstraction to summarize details instead of filtering to hide data. An abstraction-based approach could allow clinicians to more easily assemble a clinical picture, to use immediate pattern recognition strategies, and to adjust the level of displayed detail to their particular needs. It could also help the user notice unanticipated patterns and to fluidly shift attention as understanding evolves.

Association of Health Record Visualizations With Physicians' Cognitive Load When Prioritizing Hospitalized Patients

IMPORTANCE

Current electronic health records (EHRs) contribute to increased physician cognitive workload when completing clinical tasks.

OBJECTIVE

To assess the association of different design features of an EHR-based information visualization tool with the cognitive load of physicians during the clinical prioritization process.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study included a convenience sample of 29 attending physicians at Seattle Children's Hospital, a large tertiary academic pediatric hospital. Data collection took place from August 2017 through October 2017, and analysis occurred from August to October 2018.

EXPOSURE

Physician participants used 3 prototypes with novel visualizations of simulated EHR data that highlighted 1 of 3 key patient characteristics, as follows: (1) acuity, (2) clinical problem list, and (3) clinical change.

MAIN OUTCOMES AND MEASURES

Cognitive workload was measured using the NASA Task Load Index (TLX) scale (range, 1-100, with lower scores indicating lower cognitive workload). Cognitive workload was assessed for the 2 following clinical prioritization tasks: (1) finding information for a specific patient and (2) comparing results among patients for each prototype. Participants ranked 5 hypothetical patients from having the highest to the lowest priority in each design.

RESULTS

A total of 29 physician participants (15 [52%] men; 14 [48%] women; mean [range] age, 43 [35-58] years; mean [range] time in practice, 11 [3-30] years) completed the study. For task 1, the prototype highlighting clinical change was associated with lower median (interquartile range) NASA TLX scores compared with the prototype highlighting acuity (30.3 [15.2-41.6] vs 48.5 [18.7-59.3]; P = .02). For task 2, the prototype highlighting clinical change was associated with lower median (interquartile range) NASA TLX scores compared with the prototype highlighting the clinical problem list (29.1 [16.3-50.8] vs 43.5 [26.6-55.9]; P = .02). The prototype highlighting clinical change had the lowest TLX score in 17 of 29 rankings (59%) for task 1 (χ24 = 24.4; P < .001) and 18 of 29 rankings (62%) for task 2 (χ24 = 17.2; P = .002).

CONCLUSIONS AND RELEVANCE

In this study, well-designed EHR-based information visualizations that highlighted and featured clinically meaningful information patterns significantly reduced physician cognitive workload when prioritizing patient needs.

Combined Monitoring-Brain Function Monitoring and Cerebral Oximetry

Peri-operative brain function monitoring is still seen by most clinicians as complex, difficult to interpret and is therefore adopted very slowly. Current available technology mainly focusses on either a processed parameter based on the electroencephalogram to titrate anesthetics and central acting agents or on cerebral oximetry, a wider term to obtain information on the cerebral oxygen balance. There is still a lack of technological offerings that allow to monitor both entities in one device. However, there is scientific evidence that it is possible to combine measurements in an algorithmic approach that allows to better manage brain function in the surgical setting. Such integrated solutions should be made available to clinicians as they are likely to optimize patient care dependent on a sound health technology assessment.

Information needs for the rapid response team electronic clinical tool

Background

Information overload in healthcare is dangerous. It can lead to critical errors and delays. During Rapid Response Team (RRT) activations providers must make decisions quickly to rescue patients from physiological deterioration. In order to understand the clinical data required and how best to present that information in electronic systems we aimed to better assess the data needs of providers on the RRT when they respond to an event.

Methods

A web based survey to evaluate clinical data requirements was created and distributed to all RRT providers at our institution. Participants were asked to rate the importance of each data item in guiding clinical decisions during a RRT event response.

Results

There were 96 surveys completed (24.5% response rate) with fairly even distribution throughout all clinical roles on the RRT. Physiological data including heart rate, respiratory rate, and blood pressure were ranked by more than 80% of responders as being critical information. Resuscitation status was also considered critically useful by more than 85% of providers.

Conclusion

There is a limited dataset that is considered important during an RRT. The data is widely available in EMR. The findings from this study could be used to improve user-centered EMR interfaces.

Information Needs Assessment for a Medicine Ward-Focused Rounding Dashboard

To identify the routine information needs of inpatient clinicians on the general wards for the development of an electronic dashboard. Survey of internal medicine and subspecialty clinicians from March 2014-July 2014 at Saint Marys Hospital in Rochester, Minnesota. An information needs assessment was generated from all unique data elements extracted from all handoff and rounding tools used by clinicians in our ICUs and general wards. An electronic survey was distributed to 104 inpatient medical providers. 89 unique data elements were identified from currently utilized handoff and rounding instruments. All data elements were present in our multipurpose ICU-based dashboard. 42 of 104 (40 %) surveys were returned. Data elements important (50/89, 56 %) and unimportant (24/89, 27 %) for routine use were identified. No significant differences in data element ranking were observed between supervisory and nonsupervisory roles. The routine information needs of general ward clinicians are a subset of data elements used routinely by ICU clinicians. Our findings suggest an electronic dashboard could be adapted from the critical care setting to the general wards with minimal modification.

Improving Bridging from Informatics Practice to Theory

BACKGROUND

In 1962, Methods of Information in Medicine ( MIM ) began to publish papers on the methodology and scientific fundamentals of organizing, representing, and analyzing data, information, and knowledge in biomedicine and health care. Considered a companion journal, Applied Clinical Informatics ( ACI ) was launched in 2009 with a mission to establish a platform that allows sharing of knowledge between clinical medicine and health IT specialists as well as to bridge gaps between visionary design and successful and pragmatic deployment of clinical information systems. Both journals are official journals of the International Medical Informatics Association.

OBJECTIVES

As a follow-up to prior work, we set out to explore congruencies and interdependencies in publications of ACI and MIM. The objectives were to describe the major topics discussed in articles published in ACI in 2014 and to determine if there was evidence that theory in 2014 MIM publications was informed by practice described in ACI publications in any year. We also set out to describe lessons learned in the context of bridging informatics practice and theory and offer opinions on how ACI editorial policies could evolve to foster and improve such bridging.

METHODS

We conducted a retrospective observational study and reviewed all articles published in ACI during the calendar year 2014 (Volume 5) for their main theme, conclusions, and key words. We then reviewed the citations of all MIM papers from 2014 to determine if there were references to ACI articles from any year. Lessons learned in the context of bridging informatics practice and theory and opinions on ACI editorial policies were developed by consensus among the two authors.

RESULTS

A total of 70 articles were published in ACI in 2014. Clinical decision support, clinical documentation, usability, Meaningful Use, health information exchange, patient portals, and clinical research informatics emerged as major themes. Only one MIM article from 2014 cited an ACI article. There are several lessons learned including the possibility that there may not be direct links between MIM theory and ACI practice articles. ACI editorial policies will continue to evolve to reflect the breadth and depth of the practice of clinical informatics and articles received for publication. Efforts to encourage bridging of informatics practice and theory may be considered by the ACI editors.

CONCLUSIONS

The lack of direct links from informatics theory-based papers published in MIM in 2014 to papers published in ACI continues as was described for papers published during 2012 to 2013 in the two companion journals. Thus, there is little evidence that theory in MIM has been informed by practice in ACI.

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.