Subscribing to Your Patients - Reimagining the Future of Electronic Health Records

Validation of a natural language processing algorithm using national reporting data to improve identification of anesthesia-related ADVerse evENTs: The "ADVENTURE" study

BACKGROUND

Reporting and analysis of adverse events (AE) is associated with improved health system learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the feasibility of novel machine learning and natural language processing (NLP) approaches for early predictions of adverse events and provide input to direct quality improvement and patient safety initiatives.

METHODS

We used machine learning to analyze 9559 continuously reported AE by clinicians and healthcare systems to the French National Health accreditor (HAS) between January 1, 2009, and December 31, 2020 . We validated the labeling of 135,000 unique de-identified AE reports and determined the associations between different system's root causes and patient consequences. The model was validated by independent expert anesthesiologists.

RESULTS

The machine learning (ML) and Artificial Intelligence (AI) model trained on 9559 AE datasets accurately categorized 8800 (88%) of reported AE. The three most frequent AE types were "difficult orotracheal intubation" (16.9% of AE reports), "medication error" (10.5%), and "post-induction hypotension" (6.9%). The accuracy of the AI model reached 70.9% sensitivity, 96.6% specificity for "difficult intubation", 43.2% sensitivity, and 98.9% specificity for "medication error."

CONCLUSIONS

This unsupervised ML method provides an accurate, automated, AI-supported search algorithm that ranks and helps to understand complex risk patterns and has greater speed, precision, and clarity when compared to manual human data extraction. Machine learning and Natural language processing (NLP) models can effectively be used to process natural language AE reports and augment expert clinician input. This model can support clinical applications and methodological standards and used to better inform and enhance decision-making for improved risk management and patient safety.

TRIAL REGISTRATION

The study was approved by the ethics committee of the French Society of Anesthesiology (IRB 00010254-2020-20) and the CNIL (CNIL: 118 58 95) and the study was registered with ClinicalTrials.gov (NCT: NCT05185479).

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

Development of an Evidence-Based Conceptual Model of the Health Care Sector Under Digital Transformation: Integrative Review

BACKGROUND

Digital transformation is currently one of the most influential developments. It is fundamentally changing consumers' expectations and behaviors, challenging traditional firms, and disrupting numerous markets. Recent discussions in the health care sector tend to assess the influence of technological implications but neglect other factors needed for a holistic view on the digital transformation. This calls for a reevaluation of the current state of digital transformation in health care. Consequently, there is a need for a holistic view on the complex interdependencies of digital transformation in the health care sector.

OBJECTIVE

This study aimed to examine the effects of digital transformation on the health care sector. This is accomplished by providing a conceptual model of the health care sector under digital transformation.

METHODS

First, the most essential stakeholders in the health care sector were identified by a scoping review and grounded theory approach. Second, the effects on these stakeholders were assessed. PubMed, Web of Science, and Dimensions were searched for relevant studies. On the basis of an integrative review and grounded theory methodology, the relevant academic literature was systematized and quantitatively and qualitatively analyzed to evaluate the impact on the value creation of, and the relationships among, the stakeholders. Third, the findings were synthesized into a conceptual model of the health care sector under digital transformation.

RESULTS

A total of 2505 records were identified from the database search; of these, 140 (5.59%) were included and analyzed. The results revealed that providers of medical treatments, patients, governing institutions, and payers are the most essential stakeholders in the health care sector. As for the individual stakeholders, patients are experiencing a technology-enabled growth of influence in the sector. Providers are becoming increasingly dependent on intermediaries for essential parts of the value creation and patient interaction. Payers are expected to try to increase their influence on intermediaries to exploit the enormous amounts of data while seeing their business models be challenged by emerging technologies. Governing institutions regulating the health care sector are increasingly facing challenges from new entrants in the sector. Intermediaries increasingly interconnect all these stakeholders, which in turn drives new ways of value creation. These collaborative efforts have led to the establishment of a virtually integrated health care ecosystem.

CONCLUSIONS

The conceptual model provides a novel and evidence-based perspective on the interrelations among actors in the health care sector, indicating that individual stakeholders need to recognize their role in the system. The model can be the basis of further evaluations of strategic actions of actors and their effects on other actors or the health care ecosystem itself.

Improvements to emergency department length of stay and user satisfaction after implementation of an integrated consult order

Objective

Subspecialty consultation in the emergency department (ED) is a vital, albeit time consuming, part of modern medicine. Traditional consultation requires manual paging to initiate communication. Although consult orders through the electronic health record (EHR) may help, they do not facilitate 2‐way communication. However, the impact of combining these systems within the EHR is unknown. We estimated the effect of implementing an integrated paging system on ED workflow efficiency and user attitudes.

Methods

We integrated a messaging system into order entry at our tertiary care academic ED, such that placing a consult order simultaneously paged the consultant. We measured ED workflow efficiency metrics (length of stay [LOS], consult initiation time) and MD/nurse practitioner (NP)/physician assistant (PA) attitudes (perceived mis‐pages, efficiency, and workflow preference) 3 months before and 6 months after the implementation.

Results

Six months after implementation, there was 25% use of the new workflow. During the pre‐implementation phase, the median time to consult initiation and ED LOS were 150 and 621 minutes, respectively. Implementation of the order was associated with a 15‐minute reduction in median time to consult initiation (P < 0.001), and a 52‐minute reduction in median ED LOS (P < 0.001). ED MDs/NPs/PAs perceived a reduction in the rate of mis‐pages, improved efficiency, and overall preferred the new workflow.

Conclusions

We consolidated steps in the ED consult workflow using an integrated consult order, which improved user satisfaction, and reduced consult initiation time and ED LOS for patients requiring a consult at an urban tertiary care ED.

The Electronic Health Record as the Primary Data Source in a Pragmatic Trial: A Case Study

HIGHLIGHTS

Electronic health records are not a single system but a series of overlapping and legacy systems that require time and expertise to use efficiently.Commonly measured patient characteristics such as weight and body mass index are relatively easy to locate for most trial enrollees but less common characteristics, like ejection fraction, are not.Acquiring essential supplementary data-in this trial, state data on hospital admission-can be a lengthy and difficult process.

From Lock-In to Transformation: A Path-Centric Theory of Emerging Technology and Organizing

We offer a path-centric theory of emerging technology and organizing that addresses a basic question. When does emerging technology lead to transformative change? A path-centric perspective on technology focuses on the patterns of actions afforded by technology in use. We identify performing and patterning as self-reinforcing mechanisms that shape patterns of action in the domain of emerging technology and organizing. We use a dynamic simulation to show that performing and patterning can lead to a wide range of trajectories, from lock-in to transformation, depending on how emerging technology in use influences the pattern of action. When emerging technologies afford new actions that can be flexibly recombined to generate new paths, decisive transformative effects are more likely. By themselves, new affordances are not likely to generate transformation. We illustrate this theory with examples from the practice of pharmaceutical drug discovery. The path-centric perspective offers a new way to think about generativity and the role of affordances in organizing.

Partners in Narrative: Empowering Patient-Physician Partnerships in the Electronic Health Record

Amidst the chorus of valid laments about the electronic health record (EHR) are voices calling our attention to its potential to enhance transmission of information, patient communication, and decision-making. Herein, we propose ideas which, in addition, may enhance the potential of physicians and patients to become better at storytelling through the EHR. Clinicians can partner with patients to create meaningful, personalized narratives which restore inclusivity and patient agency to the EHR.

Analysing EHR navigation patterns and digital workflows among physicians during ICU pre-rounds

Background:

Some physicians in intensive care units (ICUs) report that electronic health records (EHRs) can be cumbersome and disruptive to workflow. There are significant gaps in our understanding of the physician–EHR interaction.

Objective:

To better understand how clinicians use the EHR for chart review during ICU pre-rounds through the characterisation and description of screen navigation pathways and workflow patterns.

Method:

We conducted a live, direct observational study of six physician trainees performing electronic chart review during daily pre-rounds in the 30-bed medical ICU at a large academic medical centre in the Southeastern United States. A tailored checklist was used by observers for data collection.

Results:

We observed 52 distinct live patient chart review encounters, capturing a total of 2.7 hours of pre-rounding chart review activity by six individual physicians. Physicians reviewed an average of 8.7 patients (range = 5–12), spending a mean of 3:05 minutes per patient (range = 1:34–5:18). On average, physicians visited 6.3 (±3.1) total EHR screens per patient (range = 1–16). Four unique screens were viewed most commonly, accounting for over half (52.7%) of all screen visits: results review (17.9%), summary/overview (13.0%), flowsheet (12.7%), and the chart review tab (9.1%). Navigation pathways were highly variable, but several common screen transition patterns emerged across users. Average interrater reliability for the paired EHR observation was 80.0%.

Conclusion:

We observed the physician–EHR interaction during ICU pre-rounds to be brief and highly focused. Although we observed a high degree of “information sprawl” in physicians’ digital navigation, we also identified common launch points for electronic chart review, key high-traffic screens and common screen transition patterns.

Implications:

From the study findings, we suggest recommendations towards improved EHR design.

Rationale and Design of EMPOWER, a Pragmatic Randomized Trial of Automated Hovering in Patients With Congestive Heart Failure

BACKGROUND

Congestive heart failure is a major cause of morbidity, mortality, and cost. Disease management programs have shown promise but lack firm evidence of effectiveness and scalability. We describe the motivation, design, and planned analyses of EMPOWER (Electronic Monitoring of Patients Offers Ways to Enhance Recovery), a randomized clinical trial of an innovative intervention incorporating behavioral economic principles with remote monitoring technology embedded within a healthcare system.

METHODS AND RESULTS

EMPOWER is an ongoing, pragmatic, randomized clinical trial comparing usual care to an automated hovering intervention that includes patient-level incentives for daily weight monitoring and diuretic adherence combined with automated feedback into the clinical care pathway, enabling real-time response to concerning clinical symptoms. Identification of eligible patients began in May 2016, and implementation of the intervention is feasible. Trial processes are embedded into existing clinical pathways. The primary outcome is time to readmission for any cause. Cost-effectiveness analyses are planned to evaluate the healthcare costs and health outcomes of the approach.

CONCLUSIONS

The EMPOWER trial incorporates leading-edge approaches in human motivation, derived from behavioral economics, with contemporary technology to provide scale and exception handling at low cost. The trial is also implemented within the naturalized environment of a health system, as much as possible taking advantage of the existing journeys of patients and workflows of clinicians. A goal of this pragmatic design is to limit resource utilization and also to test an intervention that would need minimal modification to be translated from research into a new way of practice.

CLINICAL TRIAL REGISTRATION

URL: https://www.clinicaltrials.gov . Unique identifier: NCT02708654.

The Value of the Surgeon Informatician

Clinical informatics is an interdisciplinary specialty that leverages big data, health information technologies, and the science of biomedical informatics within clinical environments to improve quality and outcomes in the increasingly complex and often siloed health care systems. Core competencies of clinical informatics primarily focus on clinical decision making and care process improvement, health information systems, and leadership and change management. Although the broad relevance of clinical informatics is apparent, this review focuses on its application and pertinence to the discipline of surgery, which is less well defined. In doing so, we hope to highlight the importance of the surgeon informatician. Topics covered include electronic health records, clinical decision support systems, computerized order entry, data analytics, clinical documentation, information architectures, implementation science, quality improvement, simulation, education, and telemedicine. The formal pathway for surgeons to become clinical informaticians is also discussed.

How Mobile Health Technology and Electronic Health Records Will Change Care of Patients with Parkinson's Disease

Care of patients with Parkinson’s disease (PD) will dramatically change in the upcoming years. The nationwide implementations of the patient-controlled electronic health record (EHR) and the technology-based home monitoring system will most probably be the cornerstones of this revolution. We speculate that, within the course of the next decade, EHRs will lead to a substantial empowerment of patients, and monitoring of motor and non-motor manifestations of PD will shift from the clinic to the home. As far as this can be foreseen, small, partly clothing-embedded and implanted sensor systems allowing passive (i.e., non-obtrusive) data collection will dominate the market. They will interoperate with the personal EHR and other potentially health-related electronic databases such as clinical warehouses and population health analytics platforms. Analysis software will be mainly built on artificial intelligence, and presentation of data will be intuitive. This scenario will eventually help both the patient and the medical professional by providing higher amounts of quality information about daily-relevant effects of disease and treatment, eventually allowing for a better and more personalized care.

Clinician Perspectives on Electronic Health Records, Communication, and Patient Safety Across Diverse Medical Oncology Practices

PURPOSE

We know little about how increased technological sophistication of clinical practices affects safety of chemotherapy delivery in the outpatient setting. This study investigated to what degree electronic health records (EHRs), satisfaction with technology, and quality of clinician-to-clinician communication enable a safety culture.

METHODS

We measured actions consistent with a safety culture, satisfaction with practice technology, and quality of clinician communication using validated instruments among 297 oncology nurses and prescribers in a statewide collaborative. We constructed an index to reflect practice reliance on EHRs (1 = "all paper" to 5 = "all electronic"). Linear regression models (with robust SEs to account for clustering) examined relationships between independent variables of interest and safety. Models were adjusted for clinician age.

RESULTS

The survey response rate was 68% (76% for nurses and 59% for prescribers). The mean (standard deviation) safety score was 5.3 (1.1), with a practice-level range of 4.9 to 5.4. Prescribers reported fewer safety actions than nurses. Higher satisfaction with technology and higher-quality clinician communication were significantly associated with increased safety actions, whereas increased reliance on EHRs was significantly associated with lower safety actions.

CONCLUSION

Practices vary in their performance of patient safety actions. Supporting clinicians to integrate technology and strengthen communication are promising intervention targets. The inverse relationship between reliance on EHRs and safety suggests that technology may not facilitate clinicians' ability to attend to patient safety. Efforts to improve cancer care quality should focus on more seamless integration of EHRs into routine care delivery and emphasize increasing the capacity of all care clinicians to communicate effectively and coordinate efforts when administering high-risk treatments in ambulatory settings.

Patient-centered care via health information technology: a qualitative study with experts from Israel and the U.S

Although patient-centered care (PCC) is one of the cornerstones of modern healthcare, the role that health information technology (HIT) plays in supporting PCC remains unclear. In this qualitative study, we interviewed academic and clinical experts from the US and Israel to understand to what extent current HIT systems are supportive of PCC and how PCC should be supported by HIT in the future. A maximum variation sampling approach was used to identify nine experts in both HIT and PCC from clinical and academic settings in Israel and the US. A qualitative descriptive method was used to analyze the interviews and identify major themes. Experts suggested that patient ownership of their disease is a core component of PCC. The majority of the experts agreed that in both Israel and the US, the current situation of PCC implementation is relatively poor. However, HIT should play an important role in making patients owners of their health and treatment and helping providers in delivering better PCC. Central domains of PCC via HIT were providing clear information and support for patients and promoting care that is based on patient values and preferences.

A Novel Clinical Decision Support System for Gastrointestinal Bleeding Risk Stratification in the Critically Ill

INTRODUCTION

Acid suppression therapy can reduce the development of stress and medication-related mucosal disease when prescribed appropriately. Suboptimal inpatient prescribing of acid suppression therapy therefore may lead to increased development of gastrointestinal hemorrhage in high-risk populations. The aim of this quality improvement study was to improve appropriate acid suppression therapy in patients admitted to ICUs in an academic medical center.

INTERVENTION DEVELOPMENT, IMPLEMENTATION, AND ADAPTATION

An adaptable, multifaceted implementation strategy guided by unit-based root cause analysis was initially developed in a single ICU with a high-risk population. Identifiable targets of intervention, including provider awareness, unstructured rounding protocols, and electronic communication tools, were augmented by the development of an automated alert system. This electronic dashboard risk-stratified patients based on information derived from the electronic medical record (EMR). The dashboard then offered clinical decision support. Use of the dashboard and percentage of appropriate acid suppression therapy prescriptions were tracked over time.

RESULTS

Appropriate acid suppression therapy prescribing was improved from 72.9% to 86.0% (p < 0.001).

CONCLUSION

Automated technology including an EMR-supported electronic dashboard was the foundation of successful intervention. Considering the deleterious effects of both under- and overprescribing of acid suppression therapy, particularly in high-risk patient populations, this type of technology may lead to enhanced patient outcomes.

Data-Driven Scheduling for Improving Patient Efficiency in Ophthalmology Clinics

PURPOSE

To improve clinic efficiency through development of an ophthalmology scheduling template developed using simulation models and electronic health record (EHR) data.

DESIGN

We created a computer simulation model of 1 pediatric ophthalmologist's clinic using EHR timestamp data, which was used to develop a scheduling template based on appointment length (short, medium, or long). We assessed its impact on clinic efficiency after implementation in the practices of 5 different pediatric ophthalmologists.

PARTICIPANTS

We observed and timed patient appointments in person (n = 120) and collected EHR timestamps for 2 years of appointments (n = 650). We calculated efficiency measures for 172 clinic sessions before implementation vs. 119 clinic sessions after implementation.

METHODS

We validated clinic workflow timings calculated from EHR timestamps and the simulation models based on them with observed timings. From simulation tests, we developed a new scheduling template and evaluated it with efficiency metrics before vs. after implementation.

MAIN OUTCOME MEASURES

Measurements of clinical efficiency (mean clinic volume, patient wait time, examination time, and clinic length).

RESULTS

Mean physician examination time calculated from EHR timestamps was 13.8±8.2 minutes and was not statistically different from mean physician examination time from in-person observation (13.3±7.3 minutes; P = 0.7), suggesting that EHR timestamps are accurate. Mean patient wait time for the simulation model (31.2±10.9 minutes) was not statistically different from the observed mean patient wait times (32.6±25.3 minutes; P = 0.9), suggesting that simulation models are accurate. After implementation of the new scheduling template, all 5 pediatric ophthalmologists showed statistically significant improvements in clinic volume (mean increase of 1-3 patients/session; P ≤ 0.05 for 2 providers; P ≤ 0.008 for 3 providers), whereas 4 of 5 had improvements in mean patient wait time (average improvements of 3-4 minutes/patient; statistically significant for 2 providers, P ≤ 0.008). All of the ophthalmologists' examination times remained the same before and after implementation.

CONCLUSIONS

Simulation models based on big data from EHRs can test clinic changes before real-life implementation. A scheduling template using predicted appointment length improves clinic efficiency and may generalize to other clinics. Electronic health records have potential to become tools for supporting clinic operations improvement.