Design and Implementation of an Electronic Health Record-Integrated Hypertension Management Application.

BACKGROUND: High blood pressure affects approximately 116 million adults in the United States. It is the leading risk factor for death and disability across the world. Unfortunately, over the past decade, hypertension control rates have decreased across the United States. Prediction models and clinical studies have shown that reducing clinician inertia alone is sufficient to reach the target of ≥80% blood pressure control. Digital health tools containing evidence-based algorithms that are able to reduce clinician inertia are a good fit for turning the tide in blood pressure control, but careful consideration should be taken in the design process to integrate digital health interventions into the clinical workflow. METHODS: We describe the development of a provider-facing hypertension management platform. We enumerate key steps of the development process, including needs finding, clinical workflow analysis, treatment algorithm creation, platform design and electronic health record integration. We interviewed and surveyed 5 Stanford clinicians from primary care, cardiology, and their clinical care team members (including nurses, advanced practice providers, medical assistants) to identify needs and break down the steps of clinician workflow analysis. The application design and development stage were aided by a team of approximately 15 specialists in the fields of primary care, hypertension, bioinformatics, and software development. CONCLUSIONS: Digital monitoring holds immense potential for revolutionizing chronic disease management. Our team developed a hypertension management platform at an academic medical center to address some of the top barriers to adoption and achieving clinical outcomes. The frameworks and processes described in this article may be used for the development of a diverse range of digital health tools in the cardiovascular space.

Hepatitis C Screening in Post-Baby Boomer Generation Americans: One Size Does Not Fit All.

OBJECTIVES: To analyze the impact of access to routine health care, as estimated by health insurance coverage, on hepatitis C virus (HCV) infection prevalence in US adults born after 1965 (post-baby boomer birth cohort [post-BBBC]) and to use the data to formulate strategies to optimize population screening for HCV. PATIENTS AND METHODS: Adult examinees in the National Health and Nutrition Examination Survey with available anti-HCV data were divided into era 1 (1999-2008) and era 2 (2009-2016). The prevalence of HCV infection, as defined by detectable serum HCV RNA, was determined in post-BBBC adults. In low prevalence groups, prescreening modalities were considered to increase the pretest probability. RESULTS: Of 16,966 eligible post-BBBC examinees, 0.5% had HCV infection. In both eras, more than 50% had no insurance. In era 2, HCV prevalence was 0.26% and 0.83% in those with and without insurance, respectively (P<.01). As a prescreening test, low alanine aminotransferase level (<23 U/L in women and 32 U/L in men) would identify 54% of post-BBBC adults with an extremely low (0.02%) HCV prevalence. Based on these data, a tiered approach that tests all uninsured directly for HCV and prescreens the insured with alanine aminotransferase would reduce the number to test by 56.5 million while missing less than 1% infections. CONCLUSION: For HCV elimination, passive "universal" screening in routine health care settings is insufficient, although the efficiency of screening may be improved with alanine aminotransferase prescreening. Importantly, for individuals with limited access to health care, proactive outreach programs for HCV screening are still needed.

User-centred design for machine learning in health care: a case study from care management.

OBJECTIVES: Few machine learning (ML) models are successfully deployed in clinical practice. One of the common pitfalls across the field is inappropriate problem formulation: designing ML to fit the data rather than to address a real-world clinical pain point. METHODS: We introduce a practical toolkit for user-centred design consisting of four questions covering: (1) solvable pain points, (2) the unique value of ML (eg, automation and augmentation), (3) the actionability pathway and (4) the model's reward function. This toolkit was implemented in a series of six participatory design workshops with care managers in an academic medical centre. RESULTS: Pain points amenable to ML solutions included outpatient risk stratification and risk factor identification. The endpoint definitions, triggering frequency and evaluation metrics of the proposed risk scoring model were directly influenced by care manager workflows and real-world constraints. CONCLUSIONS: Integrating user-centred design early in the ML life cycle is key for configuring models in a clinically actionable way. This toolkit can guide problem selection and influence choices about the technical setup of the ML problem.

Assessing the impact of the COVID-19 pandemic on clinician ambulatory electronic health record use.

OBJECTIVE: The COVID-19 pandemic changed clinician electronic health record (EHR) work in a multitude of ways. To evaluate how, we measure ambulatory clinician EHR use in the United States throughout the COVID-19 pandemic. MATERIALS AND METHODS: We use EHR meta-data from ambulatory care clinicians in 366 health systems using the Epic EHR system in the United States from December 2019 to December 2020. We used descriptive statistics for clinician EHR use including active-use time across clinical activities, time after-hours, and messages received. Multivariable regression to evaluate total and after-hours EHR work adjusting for daily volume and organizational characteristics, and to evaluate the association between messages and EHR time. RESULTS: Clinician time spent in the EHR per day dropped at the onset of the pandemic but had recovered to higher than prepandemic levels by July 2020. Time spent actively working in the EHR after-hours showed similar trends. These differences persisted in multivariable models. In-Basket messages received increased compared with prepandemic levels, with the largest increase coming from messages from patients, which increased to 157% of the prepandemic average. Each additional patient message was associated with a 2.32-min increase in EHR time per day (P < .001). DISCUSSION: Clinicians spent more total and after-hours time in the EHR in the latter half of 2020 compared with the prepandemic period. This was partially driven by increased time in Clinical Review and In-Basket messaging. CONCLUSIONS: Reimbursement models and workflows for the post-COVID era should account for these demands on clinician time that occur outside the traditional visit.

Assessment of Electronic Health Record Use Between US and Non-US Health Systems.

Importance: Understanding how the electronic health record (EHR) system changes clinician work, productivity, and well-being is critical. Little is known regarding global variation in patterns of use. Objective: To provide insights into which EHR activities clinicians spend their time doing, the EHR tools they use, the system messages they receive, and the amount of time they spend using the EHR after hours. Design, Setting, and Participants: This cross-sectional study analyzed the deidentified metadata of ambulatory care health systems in the US, Canada, Northern Europe, Western Europe, the Middle East, and Oceania from January 1, 2019, to August 31, 2019. All of these organizations used the EHR software from Epic Systems and represented most of Epic Systems's ambulatory customer base. The sample included all clinicians with scheduled patient appointments, such as physicians and advanced practice practitioners. Exposures: Clinician EHR use was tracked by deidentified and aggregated metadata across a variety of clinical activities. Main Outcomes and Measures: Descriptive statistics for clinician EHR use included time spent on clinical activities, note documentation (as measured by the percentage of characters in the note generated by automated or manual data entry source), messages received, and time spent after hours. Results: A total of 371 health systems were included in the sample, of which 348 (93.8%) were located in the US and 23 (6.2%) were located in other countries. US clinicians spent more time per day actively using the EHR compared with non-US clinicians (mean time, 90.2 minutes vs 59.1 minutes; P < .001). In addition, US clinicians vs non-US clinicians spent significantly more time performing 4 clinical activities: notes (40.7 minutes vs 30.7 minutes; P < .001), orders (19.5 minutes vs 8.75 minutes; P < .001), in-basket messages (12.5 minutes vs 4.80 minutes; P < .001), and clinical review (17.6 minutes vs 14.8 minutes; P = .01). Clinicians in the US composed more automated note text than their non-US counterparts (77.5% vs 60.8% of note text; P < .001) and received statistically significantly more messages per day (33.8 vs 12.8; P < .001). Furthermore, US clinicians used the EHR for a longer time after hours, logging in 26.5 minutes per day vs 19.5 minutes per day for non-US clinicians (P = .01). The median US clinician spent as much time actively using the EHR per day (90.1 minutes) as a non-US clinician in the 99th percentile of active EHR use time per day (90.7 minutes) in the sample. These results persisted after controlling for organizational characteristics, including structure, type, size, and daily patient volume. Conclusions and Relevance: This study found that US clinicians compared with their non-US counterparts spent substantially more time actively using the EHR for a wide range of clinical activities or tasks. This finding suggests that US clinicians have a greater EHR burden that may be associated with nontechnical factors, which policy makers and health system leaders should consider when addressing clinician wellness.

A computer vision system for deep learning-based detection of patient mobilization activities in the ICU.

Early and frequent patient mobilization substantially mitigates risk for post-intensive care syndrome and long-term functional impairment. We developed and tested computer vision algorithms to detect patient mobilization activities occurring in an adult ICU. Mobility activities were defined as moving the patient into and out of bed, and moving the patient into and out of a chair. A data set of privacy-safe-depth-video images was collected in the Intermountain LDS Hospital ICU, comprising 563 instances of mobility activities and 98,801 total frames of video data from seven wall-mounted depth sensors. In all, 67% of the mobility activity instances were used to train algorithms to detect mobility activity occurrence and duration, and the number of healthcare personnel involved in each activity. The remaining 33% of the mobility instances were used for algorithm evaluation. The algorithm for detecting mobility activities attained a mean specificity of 89.2% and sensitivity of 87.2% over the four activities; the algorithm for quantifying the number of personnel involved attained a mean accuracy of 68.8%.

Technology-Enabled Consumer Engagement: Promising Practices At Four Health Care Delivery Organizations.

Patients' journeys across the care continuum can be improved with patient-centered technology integrated into the care process. Misaligned financial incentives, change management challenges, and privacy concerns are some of the hurdles that have prevented health systems from deploying technology that engages patients along the care continuum. Despite these sociotechnical challenges, some health care organizations have developed innovative approaches to engaging patients. We describe promising technology-enabled consumer engagement practices at two community-based delivery organizations and two academic medical centers to demonstrate the approaches, sociotechnical challenges, and outcomes associated with their implementation. Leadership commitment and payer policies that align with the quadruple aim-enhancing patient experience, improving population health, reducing costs, and improving the work life of health care providers-would encourage further deployment and lead to greater consumer engagement along the care continuum.

Health information exchange policies of 11 diverse health systems and the associated impact on volume of exchange.

BACKGROUND: Provider organizations increasingly have the ability to exchange patient health information electronically. Organizational health information exchange (HIE) policy decisions can impact the extent to which external information is readily available to providers, but this relationship has not been well studied. OBJECTIVE: Our objective was to examine the relationship between electronic exchange of patient health information across organizations and organizational HIE policy decisions. We focused on 2 key decisions: whether to automatically search for information from other organizations and whether to require HIE-specific patient consent. METHODS: We conducted a retrospective time series analysis of the effect of automatic querying and the patient consent requirement on the monthly volume of clinical summaries exchanged. We could not assess degree of use or usefulness of summaries, organizational decision-making processes, or generalizability to other vendors. RESULTS: Between 2013 and 2015, clinical summary exchange volume increased by 1349% across 11 organizations. Nine of the 11 systems were set up to enable auto-querying, and auto-querying was associated with a significant increase in the monthly rate of exchange (P = .006 for change in trend). Seven of the 11 organizations did not require patient consent specifically for HIE, and these organizations experienced a greater increase in volume of exchange over time compared to organizations that required consent. CONCLUSIONS: Automatic querying and limited consent requirements are organizational HIE policy decisions that impact the volume of exchange, and ultimately the information available to providers to support optimal care. Future efforts to ensure effective HIE may need to explicitly address these factors.

Validation of Test Performance and Clinical Time Zero for an Electronic Health Record Embedded Severe Sepsis Alert.

BACHGROUND: Increasing use of EHRs has generated interest in the potential of computerized clinical decision support to improve treatment of sepsis. Electronic sepsis alerts have had mixed results due to poor test characteristics, the inability to detect sepsis in a timely fashion and the use of outside software limiting widespread adoption. We describe the development, evaluation and validation of an accurate and timely severe sepsis alert with the potential to impact sepsis management. OBJECTIVE: To develop, evaluate, and validate an accurate and timely severe sepsis alert embedded in a commercial EHR. METHODS: The sepsis alert was developed by identifying the most common severe sepsis criteria among a cohort of patients with ICD 9 codes indicating a diagnosis of sepsis. This alert requires criteria in three categories: indicators of a systemic inflammatory response, evidence of suspected infection from physician orders, and markers of organ dysfunction. Chart review was used to evaluate test performance and the ability to detect clinical time zero, the point in time when a patient develops severe sepsis. RESULTS: Two physicians reviewed 100 positive cases and 75 negative cases. Based on this review, sensitivity was 74.5%, specificity was 86.0%, the positive predictive value was 50.3%, and the negative predictive value was 94.7%. The most common source of end-organ dysfunction was MAP less than 70 mm/Hg (59%). The alert was triggered at clinical time zero in 41% of cases and within three hours in 53.6% of cases. 96% of alerts triggered before a manual nurse screen. CONCLUSION: We are the first to report the time between a sepsis alert and physician chart-review clinical time zero. Incorporating physician orders in the alert criteria improves specificity while maintaining sensitivity, which is important to reduce alert fatigue. By leveraging standard EHR functionality, this alert could be implemented by other healthcare systems.

Electronic Health Record-Enabled Research in Children Using the Electronic Health Record for Clinical Discovery.

Initially described more than 50 years ago, electronic health records (EHRs) are now becoming ubiquitous throughout pediatric health care settings. The confluence of increased EHR implementation and the exponential growth of digital data within them, the development of clinical informatics tools and techniques, and the growing workforce of experienced EHR users presents new opportunities to use EHRs to augment clinical discovery and improve pediatric patient care. This article reviews the basic concepts surrounding EHR-enabled research and clinical discovery, including the types and fidelity of EHR data elements, EHR data validation/corroboration, and the steps involved in analytical interrogation.

An Exponential Increase in Regional Health Information Exchange With Collaborative Policies and Technologies.

In the United States, the ability to securely exchange health information between organization has been limited by technical interoperability, patient identity matching, and variable institutional policies. Here, we examine the regional experience in a national health information exchange network by examining clinical data sharing between eleven Northern California organizations using the same health information exchange (HIE) platform between 2013-2014. We identify key policies and technologies that have led to a dramatic increase in health information exchange.

Comparison of treatment results between adult and juvenile nasopharyngeal carcinoma.

PURPOSE: Nasopharyngeal carcinoma (NPC) has a bimodal age distribution. In contrast to the adult variant, little is known about the juvenile form. This study examined the treatment results between adult (aNPC) and juvenile NPC (jNPC) patients for future treatment considerations in jNPC. METHODS AND MATERIALS: The jNPC population included 53 patients treated at two institutions between 1972 and 2004. The aNPC population included 84 patients treated at one institution. The patients had received a median dose of 66 Gy of external beam radiotherapy and 72% underwent chemotherapy. The mean follow-up for surviving patients was 12.6 years for jNPC and 6.6 years for aNPC. RESULTS: The jNPC patients presented with more advance stages than did the aNPC patients (92% vs. 67% Stage III-IV, p = .006). However, jNPC patients had significantly better overall survival (OS) than did aNPC patients. The 5-year OS rate was 71% for jNPC and 58% for aNPC (p = .03). The jNPC group also demonstrated a trend for greater relapse-free survival than the aNPC group (5-year relapse-free survival rate, 69% vs. 49%; p = .056). The pattern of failure analysis revealed that the jNPC patients had greater locoregional control and freedom from metastasis but the differences were not statistically significant. Univariate analysis for OS revealed that age group, nodal classification, and chemotherapy use were significant prognostic factors. Age group remained significant for OS on multivariate analysis, after adjusting for N classification and treatment. CONCLUSION: Despite more advance stage at presentation, jNPC patients had better survival than did aNPC patients. Future treatment strategies should take into consideration the long-term complications in these young patients.