Analysis & Visualization of EHR Patient Portal Clickstream Data

The purpose of this paper is the analysis of EHR clickstream data of patient portal to determine patient usage behavior. We present our analysis of patterns found in patient clickstream data. Using directed and undirected data mining approach, data can be explored to examine whether different patient groups appear to use the portal differently. We examine changes in usage over time, and also explore difference in usage, average number of clicks per session and time spent per page based on age and gender. We then use clustering to create groups that discriminate patients by their portal usage behavior. Knowledge of these usage patterns can help service providers understand the demographics and behavioral aspects of their patients, which in turn can help them develop, enhance and improve their systems to make the best use of these portals

Explaining the Effect of Health Status on Patient Portal Use and Health System Utilization

Since the inception of patient portals, it has been widely assumed that portals would empower patients by increasing their participation in health decisions and subsequently reducing the burden on healthcare organizations. To investigate whether this assumption holds, we analyzed the relationship between frequency of portal use and frequency of patient clinical encounters. We found that patient portal usage is proportional to patient clinical encounters, contrary to the assumption that portal use would decrease patient encounters. Patients with poorer health tended to have more encounters and subsequently more portal usage than those with better health, who had fewer encounters, indicating a possible common factor of patients’ health status. Significant differences between patients with poorer and better health status were observed for patient encounter types and portal feature usage. In addition, some portal features such as appointment scheduling, flowsheet report, medical advice, encounter details and prescription renewal were associated with fewer encounters

GUIDEseq: a bioconductor package to analyze GUIDE-Seq datasets for CRISPR-Cas nucleases

BACKGROUND: Genome editing technologies developed around the CRISPR-Cas9 nuclease system have facilitated the investigation of a broad range of biological questions. These nucleases also hold tremendous promise for treating a variety of genetic disorders. In the context of their therapeutic application, it is important to identify the spectrum of genomic sequences that are cleaved by a candidate nuclease when programmed with a particular guide RNA, as well as the cleavage efficiency of these sites. Powerful new experimental approaches, such as GUIDE-seq, facilitate the sensitive, unbiased genome-wide detection of nuclease cleavage sites within the genome. Flexible bioinformatics analysis tools for processing GUIDE-seq data are needed. RESULTS: Here, we describe an open source, open development software suite, GUIDEseq, for GUIDE-seq data analysis and annotation as a Bioconductor package in R. The GUIDEseq package provides a flexible platform with more than 60 adjustable parameters for the analysis of datasets associated with custom nuclease applications. These parameters allow data analysis to be tailored to different nuclease platforms with different length and complexity in their guide and PAM recognition sequences or their DNA cleavage position. They also enable users to customize sequence aggregation criteria, and vary peak calling thresholds that can influence the number of potential off-target sites recovered. GUIDEseq also annotates potential off-target sites that overlap with genes based on genome annotation information, as these may be the most important off-target sites for further characterization. In addition, GUIDEseq enables the comparison and visualization of off-target site overlap between different datasets for a rapid comparison of different nuclease configurations or experimental conditions. For each identified off-target, the GUIDEseq package outputs mapped GUIDE-Seq read count as well as cleavage score from a user specified off-target cleavage score prediction algorithm permitting the identification of genomic sequences with unexpected cleavage activity. CONCLUSION: The GUIDEseq package enables analysis of GUIDE-data from various nuclease platforms for any species with a defined genomic sequence. This software package has been used successfully to analyze several GUIDE-seq datasets. The software, source code and documentation are freely available at http://www.bioconductor.org/packages/release/bioc/html/GUIDEseq.html

Who Opens Alerts to Physicians? (And Who Doesn’t?)

Background: Electronic medical records (EMR) provide opportunities to implement systems of information flow, such as alerts to providers. Methods: Within a group practice with an EMR, we conducted a trial of automated alerts to the in-baskets of primary care physicians and staff when patients were discharged from hospital to home. We generated alerts for new medications or monitoring needs. Staff received alerts to schedule office visits. Using EMR “digital crumbs”, we tracked when alerts were viewed. We analyzed the impact of physician age, gender, department, and employment status (full-time, part-time) as well as patient conditions (age, gender, comorbidity, and number of office visits in the previous year) on timely opening. Results: Of 763 alerts to physicians, 616 (81%) were opened within one day. Characteristics associated with timely opening were age \u3c 50 (OR 1.7, 95% CI 1.1, 2.6) and full-time employment (OR 2.9, 95% CI 1.6, 5.2). Of 1928 alerts to staff, 1173 (61%) were opened within one day. Staff of male physicians were more likely to open the alerts within one day (OR 1.8, 95% CI 1.4, 2.4) as were working for the Family Medicine department (OR 1.9, 95% CI 1.3, 2.6) or a sub-specialty department (OR 16.6, 95% CI 2.3, 122.3). Staff of full-time physicians were less likely to open alerts (OR 0.64, 95% CI 0.47, 0.87). Adjusting for patient characteristics had no impact on results. Conclusion: Special efforts may be required to reach physicians working part-time and older physicians. Characteristics related to staff opening of alerts are specific to this group practice, but the high level of variability across physician types and departments is likely to be an issue in many settings. Design of a system directed at reaching staff quickly may require in-depth assessment of work flow and communication patterns in clinical department

The All of Us Research Program: Engaging the Community for the Future of Health

The All of Us Research Program (AoURP), funded by the National Institutes of Health, is an ambitious ten-year effort to enroll over one million participants across the country. The AoURP is a key part of the Precision Medicine Initiative and seeks to build a national cohort collecting self-reported health data, medical record data, biospecimen samples and physical measurements to accelerate precision medicine. Precision Medicine is an emerging approach for healthcare treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. Researchers at the Meyers Primary Care Institute and the University of Massachusetts Medical School have partnered with Reliant Medical Group to enroll over 10,000 participants in 5 years. The Meyers/Reliant team is actively working to engage the local community, educational institutions, and community organizations to increase awareness of the program and encourage participation. Our efforts have focused specifically on populations previously underrepresented in biomedical research, including older adults, racial and ethnic minority group members, and others. We are hoping to grow new relationships and build strong community partnerships to help us achieve our enrollment goals and communicate the great potential of the AoU Research Program to change the future of medical research with a focus on precision medicine

A Theory of Organization-EHR Affordance Actualization

While organizations implement information technology (IT) to effect change, current theories of IT-associated organizational change pay insufficient attention to the change goals, the role of IT in organizational change, and the multilevel nature of change processes. We take a fresh look at IT-associated organizational change using grounded theory methods. Our longitudinal study of an electronic health record (EHR) system implementation in a multi-site medical group found user behaviors that did not fit well with existing theories. Instead, we found that they fit better with the concept of affordances from ecological psychology. In developing our affordance-based theory of IT-associated organizational change from our field data, we discovered three gaps in the affordance literature; namely, the lack of theory for (1) the process of actualizing an affordance’s potential, (2) affordances in an organizational context, and (3) bundles of interrelated affordances. This paper extends the theory of affordances to handle these three gaps and, in doing so, develops a mid-range theory for EHR-associated organizational change in a healthcare organization. While the resulting theory is specific to EHR implementations, it offers a template for other mid-range affordance-actualization theories and a more general affordance-actualization lens. Our affordance-actualization lens considers the materiality of the IT artifact, the non-deterministic process by which IT leads to organizational effects, the multilevel nature of IT-associated change processes, and the intentionality of managers and users as agents of change, and thus addresses important criteria for theories of IT effects in organizations. The paper also provides practical guidance for implementing EHR systems and other organizational systems

Technological Resources and Personnel Costs Required to Implement an Automated Alert System for Primary Care Physicians When Patients Transition from Hospitals to Home

Background With the adoption of electronic medical records by medical group practices, there are opportunities to improve the quality of care for patients discharged from hospitals. However, there is little guidance for medical groups outside of integrated hospital systems to automate the flow of patient information during transitions in care. Objective To describe the technological resources, expertise and time needed to develop an automated system providing information to primary care physicians when their patients transition from hospitals to home. Development Within a medical group practice, we developed an automated alert system that provides notification of discharges, reminders of the need for follow-up visits, drugs added during in-patient stays, and recommendations for laboratory monitoring of high risk drugs. We tracked components of the information system required and the time spent by team members. We used US national averages of hourly wages to estimate personnel costs. Application Critical components of the information system are notifications of hospital discharges through an admission, discharge and transfer registration (ADT) interface, linkage to the practice’s scheduling system, access to information on pharmacy dispensing and lab tests, and an interface engine. Total personnel cost was $76,314. Nearly half (47%) was for 614 hours by physicians who developed content, provided overall project management, and reviewed alerts to ensure that only “actionable” alerts would be sent. Conclusion Implementing a system to provide information about patient transitions requires strong internal informatics expertise, cooperation between facilities and ambulatory providers, development of electronic linkages, and extensive commitment of physician time

Improving Rates of Influenza Vaccination Through Electronic Health Record Portal Messages, Interactive Voice Recognition Calls and Patient-Enabled Electronic Health Record Updates: Protocol for a Randomized Controlled Trial

BACKGROUND: Clinical decision support (CDS), including computerized reminders for providers and patients, can improve health outcomes. CDS promoting influenza vaccination, delivered directly to patients via an electronic health record (EHR) patient portal and interactive voice recognition (IVR) calls, offers an innovative approach to improving patient care. OBJECTIVE: To test the effectiveness of an EHR patient portal and IVR outreach to improve rates of influenza vaccination in a large multispecialty group practice in central Massachusetts. METHODS: We describe a nonblinded, randomized controlled trial of EHR patient portal messages and IVR calls designed to promote influenza vaccination. In our preparatory phase, we conducted qualitative interviews with patients, providers, and staff to inform development of EHR portal messages with embedded questionnaires and IVR call scripts. We also provided practice-wide education on influenza vaccines to all physicians and staff members, including information on existing vaccine-specific EHR CDS. Outreach will target adult patients who remain unvaccinated for more than 2 months after the start of the influenza season. Using computer-generated randomization and a factorial design, we will assign 20,000 patients who are active users of electronic patient portals to one of the 4 study arms: (1) receipt of a portal message promoting influenza vaccines and offering online appointment scheduling; (2) receipt of an IVR call with similar content but without appointment facilitation; (3) both (1) and (2); or (4) neither (1) nor (2) (usual care). We will randomize patients without electronic portals (10,000 patients) to (1) receipt of IVR call or (2) usual care. Both portal messages and IVR calls promote influenza vaccine completion. Our primary outcome is percentage of eligible patients with influenza vaccines administered at our group practice during the 2014-15 influenza season. Both outreach methods also solicit patient self-report on influenza vaccinations completed outside the clinic or on barriers to influenza vaccination. Self-reported data from both outreach modes will be uploaded into the EHR to increase accuracy of existing provider-directed EHR CDS (vaccine alerts). RESULTS: With our proposed sample size and using a factorial design, power calculations using baseline vaccination rate estimates indicated that 4286 participants per arm would give 80% power to detect a 3% improvement in influenza vaccination rates between groups (alpha=.05; 2-sided). Intention-to-treat unadjusted chi-square analyses will be performed to assess the impact of portal messages, either alone or in combination with the IVR call, on influenza vaccination rates. The project was funded in January 2014. Patient enrollment for the project described here completed in December 2014. Data analysis is currently under way and first results are expected to be submitted for publication in 2016. CONCLUSIONS: If successful, this study\u27s intervention may be adapted by other large health care organizations to increase vaccination rates among their eligible patients. CLINICALTRIAL: ClinicalTrials.gov NCT02266277; https://clinicaltrials.gov/ct2/show/NCT02266277 (Archived by WebCite at http://www.webcitation.org/6fbLviHLH)

Use of Electronic Health Record Access and Audit Logs to Identify Physician Actions Following Noninterruptive Alert Opening: Descriptive Study

BACKGROUND: Electronic health record (EHR) access and audit logs record behaviors of providers as they navigate the EHR. These data can be used to better understand provider responses to EHR-based clinical decision support (CDS), shedding light on whether and why CDS is effective. OBJECTIVE: This study aimed to determine the feasibility of using EHR access and audit logs to track primary care physicians\u27 (PCPs\u27) opening of and response to noninterruptive alerts delivered to EHR InBaskets. METHODS: We conducted a descriptive study to assess the use of EHR log data to track provider behavior. We analyzed data recorded following opening of 799 noninterruptive alerts sent to 75 PCPs\u27 InBaskets through a prior randomized controlled trial. Three types of alerts highlighted new medication concerns for older patients\u27 posthospital discharge: information only (n=593), medication recommendations (n=37), and test recommendations (n=169). We sought log data to identify the person opening the alert and the timing and type of PCPs\u27 follow-up EHR actions (immediate vs by the end of the following day). We performed multivariate analyses examining associations between alert type, patient characteristics, provider characteristics, and contextual factors and likelihood of immediate or subsequent PCP action (general, medication-specific, or laboratory-specific actions). We describe challenges and strategies for log data use. RESULTS: We successfully identified the required data in EHR access and audit logs. More than three-quarters of alerts (78.5%, 627/799) were opened by the PCP to whom they were directed, allowing us to assess immediate PCP action; of these, 208 alerts were followed by immediate action. Expanding on our analyses to include alerts opened by staff or covering physicians, we found that an additional 330 of the 799 alerts demonstrated PCP action by the end of the following day. The remaining 261 alerts showed no PCP action. Compared to information-only alerts, the odds ratio (OR) of immediate action was 4.03 (95% CI 1.67-9.72) for medication-recommendation and 2.14 (95% CI 1.38-3.32) for test-recommendation alerts. Compared to information-only alerts, ORs of medication-specific action by end of the following day were significantly greater for medication recommendations (5.59; 95% CI 2.42-12.94) and test recommendations (1.71; 95% CI 1.09-2.68). We found a similar pattern for OR of laboratory-specific action. We encountered 2 main challenges: (1) Capturing a historical snapshot of EHR status (number of InBasket messages at time of alert delivery) required incorporation of data generated many months prior with longitudinal follow-up. (2) Accurately interpreting data elements required iterative work by a physician/data manager team taking action within the EHR and then examining audit logs to identify corresponding documentation. CONCLUSIONS: EHR log data could inform future efforts and provide valuable information during development and refinement of CDS interventions. To address challenges, use of these data should be planned before implementing an EHR-based study.

Electronic Health Record Portal Messages and Interactive Voice Response Calls to Improve Rates of Early Season Influenza Vaccination: Randomized Controlled Trial

BACKGROUND: Patient reminders for influenza vaccination, delivered via an electronic health record patient portal and interactive voice response calls, offer an innovative approach to engaging patients and improving patient care. OBJECTIVE: The goal of this study was to test the effectiveness of portal and interactive voice response outreach in improving rates of influenza vaccination by targeting patients in early September, shortly after vaccinations became available. METHODS: Using electronic health record portal messages and interactive voice response calls promoting influenza vaccination, outreach was conducted in September 2015. Participants included adult patients within a large multispecialty group practice in central Massachusetts. Our main outcome was electronic health record-documented early influenza vaccination during the 2015-2016 influenza season, measured in November 2015. We randomly assigned all active portal users to 1 of 2 groups: (1) receiving a portal message promoting influenza vaccinations, listing upcoming clinics, and offering online scheduling of vaccination appointments (n=19,506) or (2) receiving usual care (n=19,505). We randomly assigned all portal nonusers to 1 of 2 groups: (1) receiving interactive voice response call (n=15,000) or (2) receiving usual care (n=43,596). The intervention also solicited patient self-reports on influenza vaccinations completed outside the clinic. Self-reported influenza vaccination data were uploaded into the electronic health records to increase the accuracy of existing provider-directed electronic health record clinical decision support (vaccination alerts) but were excluded from main analyses. RESULTS: Among portal users, 28.4% (5549/19,506) of those randomized to receive messages and 27.1% (5294/19,505) of the usual care group had influenza vaccinations documented by November 2015 (P=.004). In multivariate analysis of portal users, message recipients were slightly more likely to have documented vaccinations when compared to the usual care group (OR 1.07, 95% CI 1.02-1.12). Among portal nonusers, 8.4% (1262/15,000) of those randomized to receive calls and 8.2% (3586/43,596) of usual care had documented vaccinations (P=.47), and multivariate analysis showed nonsignificant differences. Over half of portal messages sent were opened (10,112/19,479; 51.9%), and over half of interactive voice response calls placed (7599/14,984; 50.7%) reached their intended target, thus we attained similar levels of exposure to the messaging for both interventions. Among portal message recipients, 25.4% of message openers (2570/10,112) responded to a subsequent question on receipt of influenza vaccination; among interactive voice response recipients, 72.5% of those reached (5513/7599) responded to a similar question. CONCLUSIONS: Portal message outreach to a general primary care population achieved a small but statistically significant improvement in rates of influenza vaccination (OR 1.07, 95% CI 1.02-1.12). Interactive voice response calls did not significantly improve vaccination rates among portal nonusers (OR 1.03, 95% CI 0.96-1.10). Rates of patient engagement with both modalities were favorable. TRIAL REGISTRATION: ClinicalTrials.gov NCT02266277; https://clinicaltrials.gov/ct2/show/NCT02266277. Lloyd Fisher, Peggy Preusse, Devi Sundaresan, Lawrence Garber, Kathleen M Mazor, Sarah L Cutrona. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.09.2020