Clinical decision support with a comprehensive in-EHR patient tracking system improves genetic testing follow up.

OBJECTIVE: We sought to develop and evaluate an electronic health record (EHR) genetic testing tracking system to address the barriers and limitations of existing spreadsheet-based workarounds. MATERIALS AND METHODS: We evaluated the spreadsheet-based system using mixed effects logistic regression to identify factors associated with delayed follow up. These factors informed the design of an EHR-integrated genetic testing tracking system. After deployment, we assessed the system in 2 ways. We analyzed EHR access logs and note data to assess patient outcomes and performed semistructured interviews with users to identify impact of the system on work. RESULTS: We found that patient-reported race was a significant predictor of documented genetic testing follow up, indicating a possible inequity in care. We implemented a CDS system including a patient data capture form and management dashboard to facilitate important care tasks. The system significantly sped review of results and significantly increased documentation of follow-up recommendations. Interviews with key system users identified a range of sociotechnical factors (ie, tools, tasks, collaboration) that contribute to safer and more efficient care. DISCUSSION: Our new tracking system ended decades of workarounds for identifying and communicating test results and improved clinical workflows. Interview participants related that the system decreased cognitive and time burden which allowed them to focus on direct patient interaction. CONCLUSION: By assembling a multidisciplinary team, we designed a novel patient tracking system that improves genetic testing follow up. Similar approaches may be effective in other clinical settings.

Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs.

OBJECTIVE: Patient attribution, or the process of attributing patient-level metrics to specific providers, attempts to capture real-life provider-patient interactions (PPI). Attribution holds wide-ranging importance, particularly for outcomes in graduate medical education, but remains a challenge. We developed and validated an algorithm using EHR data to identify pediatric resident PPIs (rPPIs). METHODS: We prospectively surveyed residents in three care settings to collect self-reported rPPIs. Participants were surveyed at the end of primary care clinic, emergency department (ED), and inpatient shifts, shown a patient census list, asked to mark the patients with whom they interacted, and encouraged to provide a short rationale behind the marked interaction. We extracted routine EHR data elements, including audit logs, note contribution, order placement, care team assignment, and chart closure, and applied a logistic regression classifier to the data to predict rPPIs in each care setting. We also performed a comment analysis of the resident-reported rationales in the inpatient care setting to explore perceived patient interactions in a complicated workflow. RESULTS: We surveyed 81 residents over 111 shifts and identified 579 patient interactions. Among EHR extracted data, time-in-chart was the best predictor in all three care settings (primary care clinic: odds ratio [OR] = 19.36, 95% confidence interval [CI]: 4.19-278.56; ED: OR = 19.06, 95% CI: 9.53-41.65' inpatient: OR = 2.95, 95% CI: 2.23-3.97). Primary care clinic and ED specific models had c-statistic values > 0.98, while the inpatient-specific model had greater variability (c-statistic = 0.89). Of 366 inpatient rPPIs, residents provided rationales for 90.1%, which were focused on direct involvement in a patient's admission or transfer, or care as the front-line ordering clinician (55.6%). CONCLUSION: Classification models based on routinely collected EHR data predict resident-defined rPPIs across care settings. While specific to pediatric residents in this study, the approach may be generalizable to other provider populations and scenarios in which accurate patient attribution is desirable.

Early experiences with combined fellowship training in clinical informatics.

Given the ubiquitous nature of information systems in modern health care, interest in the pursuit of formal training in clinical informatics is increasing. This interest is not restricted to generalists-informatics training is increasingly being sought by future subspecialists. The traditional structure of Accreditation Council on Graduate Medical Education subspecialty training requires completion of both clinical and clinical informatics fellowship programs, and understandably lacks appeal due to the time commitment required. One approach to encourage clinical informatics training is to integrate it with clinical fellowships in order to confer dual-board eligibility. In this perspective, we describe 3 successful petitions for combined training in clinical informatics in order to support other programs and the American Board of Preventive Medicine in establishing pathways for training subspecialists in clinical informatics.

A Model for Clinical Informatics Education for Residents: Addressing an Unmet Need.

Opportunities for education in clinical informatics exist throughout the spectrum of formal education extending from high school to postgraduate training. However, physicians in residency represent an underdeveloped source of potential informaticians. Despite the rapid growth of accredited fellowship programs since clinical informatics became a board-eligible subspecialty in 2011, few resident physicians are aware of their role at the intersection of clinical medicine and health information technology or associated opportunities. In an effort to educate and engage residents in clinical informatics, Children's Hospital of Philadelphia has developed a three-pronged model: (1) an elective rotation with hands-on project experience; (2) a longitudinal experience that offers increased exposure and mentorship; and (3) a resident founded and led working group in clinical informatics. We describe resident participation in these initiatives and lessons learned, as well as resident perceptions of how these components have positively influenced informatics knowledge and career choices. Since inception of this model, five residents have pursued the clinical informatics fellowship. This educational model supports resident involvement in hospital-wide informatics efforts with tangible projects and promotes wider engagement through educational opportunities commensurate with the resident's level of interest.

Characteristics of the National Applicant Pool for Clinical Informatics Fellowships (2016-2017).

We conducted a national study to assess the numbers and diversity of applicants for 2016 and 2017 clinical informatics fellowship positions. In each year, we collected data on the number of applications that programs received from candidates who were ultimately successful vs. unsuccessful. In 2017, we also conducted an anonymous applicant survey. Successful candidates applied to an average of 4.2 and 5.5 programs for 2016 and 2017, respectively. In the survey, unsuccessful candidates reported applying to fewer programs. Assuming unsuccessful candidates submitted between 2-5 applications each, the total applicant pool numbered 42-69 for 2016 (competing for 24 positions) and 52-85 for 2017 (competing for 30 positions). Among survey respondents (n=33), 24% were female, 1 was black and none were Hispanic. We conclude that greater efforts are needed to enhance interest in clinical informatics among medical students and residents, particularly among women and members of underrepresented minority groups.

Evaluation of a Mobile Health Approach to Tuberculosis Contact Tracing in Botswana.

Tuberculosis (TB) contact tracing is typically conducted in resource-limited settings with paper forms, but this approach may be limited by inefficiencies in data collection, storage, and retrieval and poor data quality. In Botswana, we developed, piloted, and evaluated a mobile health (mHealth) approach to TB contact tracing that replaced the paper form-based approach for a period of six months. For both approaches, we compared the time required to complete TB contact tracing and the quality of data collected. For the mHealth approach, we also administered the Computer System Usability Questionnaire to 2 health care workers who used the new approach, and we identified and addressed operational considerations for implementation. Compared to the paper form-based approach, the mHealth approach reduced the median time required to complete TB contact tracing and improved data quality. The mHealth approach also had favorable overall rating, system usefulness, information quality, and interface quality scores on the Computer System Usability Questionnaire. Overall, the mHealth approach to TB contact tracing improved on the paper form-based approach used in Botswana. This new approach may similarly benefit TB contact tracing efforts in other resource-limited settings.

After-Hours Call Center Triage of Pediatric Head Injury: Outcomes After a Concussion Initiative.

OBJECTIVE: The aim of the study was to characterize referral patterns and medical outcomes of children with head injury triaged by an after-hours call center of a large urban pediatric network, both before and after an institutional concussion initiative. The initiative included a revised call center triage algorithm referring patients with a suspected concussion to see a primary care provider (PCP) within 24 hours, concussion-specific continuing education for medical providers, and a new concussion information Web site. METHODS: Patients aged 5 to 18 years with head injury using the after-hours call center were identified by retrospective review of electronic medical records before (2011) and after (2012) the initiative. A random 50% sample was taken from each year for further analysis. RESULTS: A total of 127 and 159 eligible patient encounters were randomly selected from 2011 to 2012, respectively. From 2011 to 2012, PCP referrals significantly increased from 7% (95% confidence interval [CI], 4%-13%) to 38% (95% CI, 31%-45%), P < 0.001. Concussion diagnoses also significantly increased from 35% (95% CI, 27%-44%) to 58% (95% CI, 50%-66%), P < 0.001. Emergency department referrals and head computed tomography scans decreased but the differences were not statistically significant. No patients had intracranial injury on computed tomography. Most injuries were not sports related. CONCLUSIONS: After an institutional concussion initiative including implementation of a revised head trauma telephone triage algorithm, more head injuries were evaluated by PCPs and more concussions were ultimately diagnosed without an increase in emergency department referrals. Clinicians can benefit from continuing education and infrastructure to aid in initial concussion diagnosis and management.

mHealth to revolutionize information retrieval in low and middle income countries: introduction and proposed solutions using Botswana as reference point.

Information retrieval (IR) practice is invaluable in health care, where the growth of medical knowledge has long surpassed human memory capabilities, and health care workers often have unmet information needs. While the information and communications technology (ICT) revolution is improving, IR in the Western world, the global digital divide has never been wider. Low and Middle Income Countries (LMICs) have the least advanced ICT infrastructure and service provision, and are also burdened with the majority of the world's health issues and severe shortages of health care workers. Initiatives utilizing mobile technology in healthcare and public health (mHealth) have shown potential at addressing these inequalities and challenges. Using Botswana as a reference point, this paper aims to broadly describe the healthcare and ICT challenges facing LMICs, the promise of mHealth as a field in health informatics, and then propose health informatics solutions that specifically address IR content and needs. One solution proposes utilizing Unstructured Supplementary Service Data (USSD) for accessing treatment guidelines, and the other solution outlines applications of smart devices for IR.

A mobile health approach to tuberculosis contact tracing in resource-limited settings.

Tuberculosis remains a leading cause of morbidity and mortality worldwide. In 2011, there were 8.7 million new cases and 1.4 million deaths from the disease, with >95% of these deaths taking place in low- and middle-income countries [1]. Contact tracing prevents the spread of tuberculosis by identifying and screening a case's contacts and referring symptomatic individuals to health care providers. Traditionally, contact tracing has been conducted with paper forms, which can lead to considerable inefficiencies in data collection, storage, and retrieval. These inefficiencies are problematic as tuberculosis can continue to spread if disruption of disease transmission is delayed. Mobile health approaches to tuberculosis contact tracing remain largely unaddressed and limited to management and monitoring of multidrug-resistant tuberculosis [2].To address these limitations, a mobile health application that digitizes and automates contact tracing was developed. This poster presents work currently underway to evaluate this new approach in Botswana, which has the tenth highest incidence rate of tuberculosis in the world [3]. Operational considerations for implementing a mobile health approach to contact tracing in resource-limited settings are also presented.

Impact of electronic health record-based alerts on influenza vaccination for children with asthma.

OBJECTIVE: The goal was to assess the impact of influenza vaccine clinical alerts on missed opportunities for vaccination and on overall influenza immunization rates for children and adolescents with asthma. METHODS: A prospective, cluster-randomized trial of 20 primary care sites was conducted between October 1, 2006, and March 31, 2007. At intervention sites, electronic health record-based clinical alerts for influenza vaccine appeared at all office visits for children between 5 and 19 years of age with asthma who were due for vaccine. The proportion of captured immunization opportunities at visits and overall rates of complete vaccination for patients at intervention and control sites were compared with those for the previous year, after standardization for relevant covariates. The study had >80% power to detect an 8% difference in the change in rates between the study and baseline years at intervention versus control practices. RESULTS: A total of 23 418 visits and 11 919 children were included in the study year and 21 422 visits and 10 667 children in the previous year. The majority of children were male, 5 to 9 years of age, and privately insured. With standardization for selected covariates, captured vaccination opportunities increased from 14.4% to 18.6% at intervention sites and from 12.7% to 16.3% at control sites, a 0.3% greater improvement. Standardized influenza vaccination rates improved 3.4% more at intervention sites than at control sites. The 4 practices with the greatest increases in rates (>or=11%) were all in the intervention group. Vaccine receipt was more common among children who had been vaccinated previously, with increasing numbers of visits, with care early in the season, and at preventive versus acute care visits. CONCLUSIONS: Clinical alerts were associated with only modest improvements in influenza vaccination rates.

Effectiveness of a clinical/billing alert system vs education alone in assisting physicians to correctly bill for asthma nebulization treatments in the pediatric office setting.

Missed billing opportunities can be a negative outcome of the implementation of non-interfaced clinical and billing information systems. We describe a decision support system that prompts physicians with suggested billing options at the point of ordering nebulized medications. A baseline rate of 70% missed billing opportunities decreased to 38% with education alone and to 7% with an alert system. Decision support can best support billing opportunities when automated and appearing at the point of care.

Identifying factors predicting immunization delay for children followed in an urban primary care network using an electronic health record.

OBJECTIVE: An opportunity exists to use increasingly prevalent electronic health records to efficiently gather immunization, clinical, and demographic data to assess and subsequently reduce barriers to immunization. The objective of this study was to use data entered at the point of care within an electronic health record to identify factors that predispose children in an inner-city population to immunization delay. METHODS: Retrospective cohort data from an electronic health record were used to evaluate the association between demographic, clinical, and immunization variables on immunization delay at 24 months. Patients 2 to 5 years old as of May 31, 2003, with an office visit between May 31, 2002, and May 31, 2003, were selected (N = 5464). Univariate and multivariable models were developed to predict vaccination delay at 24 months per the Advisory Committee on Immunization Practices guidelines. RESULTS: Overall up-to-date immunization rates at 3, 7, 13, and 24 months were 75%, 45%, 82%, and 71%. Multivariable models using electronic health record data showed that early immunization status was the strongest predictor of immunization delay at 24 months. Multivariate analysis revealed that children who were inadequately immunized at 3 months of age were more than 4.5 times as likely to be immunization delayed at 24 months. In this analysis, patient and caregiver factors associated with immunization delay included insurance status and nonparent caregiver. Children who were premature were less likely to be delayed. CONCLUSIONS: Using an electronic health record with information entered at the point of care, we found that early immunization status is a strong predictor of immunization delay for young children that can be identified as early as 3 months of age. Electronic health records may prove useful to clinicians and health systems in identifying children at high risk for immunization delay.

Children referred to an emergency department by an after-hours call center: complaint-specific analysis.

BACKGROUND AND OBJECTIVES: Approximately 20% of phone calls to after-hours call centers result in referrals to the emergency department (ED), but data regarding ED management and disposition are lacking. We sought to determine the acuity of illness of referred children as reflected by triage classifications and need for therapeutic interventions, diagnostic testing, and hospitalization, and to stratify the analysis of ED management and dispositions by chief complaints. DESIGN AND METHODS: Patients referred to the ED by the after-hours call centers, without physician consultation, were identified. The 4 most common groups of chief complaints resulting in ED referral were determined, and the records of these children were analyzed. RESULTS: The 525 patients with chief complaints related to the following organ systems were studied: lower respiratory tract, 263 (50%); gastrointestinal, 104 (20%); head, ears, eyes, nose, and throat, 84 (16%); and upper respiratory tract, 74 (14%). The proportion of children referred for lower respiratory tract complaints who received the after-hours call centers call dispositions (99%) or ED triage classifications (38%) of highest priorities, or who required therapeutic interventions (73%), diagnostic testing (40%), or hospitalization (22%) was significantly higher than for all other categories. Thirteen percent with gastrointestinal complaints, referred primarily for dehydration, required intravenous fluids, and 2% of head, ears, eyes, nose, and throat patients required hospitalization. CONCLUSIONS: Children referred to the ED for illnesses related to the lower respiratory tract, principally wheezing, had illnesses of high acuity. On the other hand, current criteria for ED referral for children in the gastrointestinal, head, ears, eyes, nose, and throat, and upper respiratory tract categories result in the referral of many children with nonurgent problems. These data support a reassessment of current referral practices for children with these complaints.

Outcomes of children referred to an emergency department by an after-hours call center.

BACKGROUND AND OBJECTIVES: After-hours call centers are increasingly prevalent, yet there is little patient outcomes data. We sought to determine compliance with referral to an emergency department (ED) and describe outcomes and clinical characteristics of referred patients including triage classifications, therapeutic interventions, diagnostic testing, diagnoses, and hospitalization rates. DESIGN AND PARTICIPANTS: A retrospective cohort design. We studied hospital-based primary care patients who were referred to the ED of a tertiary care children's hospital by the call center, without physician consultation. RESULTS: Of the 8265 telephone calls to the call center, 1473 (18%) children were referred to the ED, and 931 (63%) were compliant. Compliant patients were significantly younger (P = 0.01) and were more likely to have been referred to the ED immediately (P = 0.01) compared with noncompliant patients. Among compliant patients, 48% were classified as nonurgent at ED triage; of these, half did not have a therapeutic intervention or diagnostic test or require hospitalization. Overall, therapeutic interventions were administered to 44%, diagnostic tests were performed for 45%, and 13% were hospitalized. Compared to children referred immediately to the ED, the proportion instructed to go to the ED within 4 hours of the telephone call received significantly lower ED triage classifications (93% vs. 77%, P < 0.00001) and hospitalizations (15% vs. 6%, P = 0.001). CONCLUSIONS: In this study, we report the outcomes and clinical characteristics of children referred to the ED by an after-hours call center. Data such as these may be used by call centers in the assessment of management advice and referral practices.