Person:

Mandl, Kenneth

Background: Data stored in personally controlled health records (PCHRs) may hold value for clinicians and public health entities, if patients and their families will share them. We sought to characterize consumer willingness and unwillingness (reticence) to share PCHR data across health topics, and with different stakeholders, to advance understanding of this issue. Methods: Cross-sectional 2009 Web survey of repeat PCHR users who were patients over 18 years old or parents of patients, to assess willingness to share their PCHR data with an-out-of-hospital provider to support care, and the state/local public health authority to support monitoring; the odds of reticence to share PCHR information about ten exemplary health topics were estimated using a repeated measures approach. Results: Of 261 respondents (56% response rate), more reported they would share all information with the state/local public health authority (63.3%) than with an out-of-hospital provider (54.1%) (OR 1.5, 95% CI 1.1, 1.9; p = .005); few would not share any information with these parties (respectively, 7.9% and 5.2%). For public health sharing, reticence was higher for most topics compared to contagious illness (ORs 4.9 to 1.4, all p-values < .05), and reflected concern about anonymity (47.2%), government insensitivity (41.5%), discrimination (24%). For provider sharing, reticence was higher for all topics compared to contagious illness (ORs 6.3 to 1.5, all p-values < .05), and reflected concern for relevance (52%), disclosure to insurance (47.6%) and/or family (20.5%). Conclusions: Pediatric patients and their families are often willing to share electronic health information to support health improvement, but remain cautious. Robust trust models for PCHR sharing are needed.

Objective: Test a novel health monitoring approach by engaging an international online diabetes social network (SN) in consented health surveillance. Methods: Collection of structured self-reports about preventive and self-care practices and health status using a software application (“app”) that supports SN-mediated health research. Comparison of SN measures by diabetes type; and, SN with Behavioral Risk Factor Surveillance System (BRFSS) data, for US-residing insulin dependent respondents, using logistic regression. Results: Of 2,414 SN app users, 82% (n=1979) provided an A1c and 41% (n=996) completed a care survey of which 931 have diabetes. Of these: 65% and 41% were immunized against influenza and pneumonia respectively, 90% had their cholesterol checked, 82% and 66%, had their eyes and feet checked, respectively. Type 1/LADA respondents were more likely than Type 2/pre-diabetic respondents to report all five recommended practices (Adjusted OR (95% CI) 2.2 (1.5, 3.2)). Past year self-care measures were: 58% self-monitored their blood glucose (SMBG) ≥ 5 times daily, 37% saw their diabetes nutritionist, 56% saw a diabetes nurse educator, 53% saw a doctor for their diabetes ≥ 4 times. Reports of health status did not differ by diabetes type in the SN sample. The SN group was more likely than the BRFSS comparator group to use all five preventive care practices (Adjusted OR (95% CI) 1.8 (1.4, 2.1) and SMBG ≥ 5 times daily (Adjusted OR (95% CI) 10.1 (6.8, 14.9). Conclusions: Rapid assessment of diabetes care practices using a novel, SN-mediated approach can extend the capability of standard health surveillance systems.

Background: We sought to measure population-level adherence to antihyperlipidemics, antihypertensives, and oral hypoglycemics, and to develop a model for early identification of subjects at high risk of long-term poor adherence. Methods Prescription-filling data for 2 million subjects derived from a payor's insurance claims were used to evaluate adherence to three chronic drugs over 1 year. We relied on patterns of prescription fills, including the length of gaps in medication possession, to measure adherence among subjects and to build models for predicting poor long-term adherence. Results: All prescription fills for a specific drug were sequenced chronologically into drug eras. 61.3% to 66.5% of the prescription patterns contained medication gaps >30 days during the first year of drug use. These interrupted drug eras include long-term discontinuations, where the subject never again filled a prescription for any drug in that category in the dataset, which represent 23.7% to 29.1% of all drug eras. Among the prescription-filling patterns without large medication gaps, 0.8% to 1.3% exhibited long-term poor adherence. Our models identified these subjects as early as 60 days after the first prescription fill, with an area under the curve (AUC) of 0.81. Model performance improved as the predictions were made at later time-points, with AUC values increasing to 0.93 at the 120-day time-point. Conclusions: Dispensed medication histories (widely available in real time) are useful for alerting providers about poorly adherent patients and those who will be non-adherent several months later. Efforts to use these data in point of care and decision support facilitating patient are warranted.

Background: The $1.1 billion investment in comparative effectiveness research will reshape the evidence-base supporting decisions about treatment effectiveness, safety, and cost. Defining the current prevalence and characteristics of comparative effectiveness (CE) research will enable future assessments of the impact of this program. Methods: We conducted an observational study of clinical trials addressing priority research topics defined by the Institute of Medicine and conducted in the US between 2007 and 2010. Trials were identified in ClinicalTrials.gov. Main outcome measures were the prevalence of comparative effectiveness research, nature of comparators selected, funding sources, and impact of these factors on results. Results: 231 (22.3%; 95% CI 19.8%–24.9%) studies were CE studies and 804 (77.7%; 95% CI, 75.1%–80.2%) were non-CE studies, with 379 (36.6%; 95% CI, 33.7%–39.6%) employing a placebo control and 425 (41.1%; 95% CI, 38.1%–44.1%) no control. The most common treatments examined in CE studies were drug interventions (37.2%), behavioral interventions (28.6%), and procedures (15.6%). Study findings were favorable for the experimental treatment in 34.8% of CE studies and greater than twice as many (78.6%) non-CE studies (P<0.001). CE studies were more likely to receive government funding (P = 0.003) and less likely to receive industry funding (P = 0.01), with 71.8% of CE studies primarily funded by a noncommercial source. The types of interventions studied differed based on funding source, with 95.4% of industry trials studying a drug or device. In addition, industry-funded CE studies were associated with the fewest pediatric subjects (P<0.001), the largest anticipated sample size (P<0.001), and the shortest study duration (P<0.001). Conclusions: In this sample of studies examining high priority areas for CE research, less than a quarter are CE studies and the majority is supported by government and nonprofits. The low prevalence of CE research exists across CE studies with a broad array of interventions and characteristics.

Background: Surveillance and response to diabetes may be accelerated through engaging online diabetes social networks (SNs) in consented research. We tested the willingness of an online diabetes community to share data for public health research by providing members with a privacy-preserving social networking software application for rapid temporalgeographic surveillance of glycemic control. Methods and Findings: SN-mediated collection of cross-sectional, member-reported data from an international online diabetes SN entered into a software applicaction we made available in a ‘‘Facebook-like’’ environment to enable reporting, charting and optional sharing of recent hemoglobin A1c values through a geographic display. Self-enrollment by 17% (n = 1,136) of n = 6,500 active members representing 32 countries and 50 US states. Data were current with 83.1% of most recent A1c values reported obtained within the past 90 days. Sharing was high with 81.4% of users permitting data donation to the community display. 34.1% of users also displayed their A1cs on their SN profile page. Users selecting the most permissive sharing options had a lower average A1c (6.8%) than users not sharing with the community (7.1%, p = .038). 95% of users permitted re-contact. Unadjusted aggregate A1c reported by US users closely resembled aggregate 2007–2008 NHANES estimates (respectively, 6.9% and 6.9%, p = 0.85). Conclusions: Success within an early adopter community demonstrates that online SNs may comprise efficient platforms for bidirectional communication with and data acquisition from disease populations. Advancing this model for cohort and translational science and for use as a complementary surveillance approach will require understanding of inherent selection and publication (sharing) biases in the data and a technology model that supports autonomy, anonymity and privacy.

This study aimed to comprehensively describe prevalence and temporal dispensing patterns for medications prescribed to children and adolescents in the United States. Participants were 1.6 million children (49% female) under 18 years old enrolled in a nation-wide, employer-provided insurance plan. All medication claims from 1999–2006 were reviewed retrospectively. Drugs were assigned to 16 broad therapeutic categories. Effects of trend over time, seasonality, age and gender on overall and within category prevalence were examined. Results: Mean monthly prevalence for dispensed medications was 23.5% (range 19.4–27.5), with highest rates in winter and lowest in July. The age group with the highest prevalence was one-year-old children. On average each month, 17.1% of all children were dispensed a single drug and 6.4% were dispensed two or more. Over time, prevalence for two or more drugs did not change, but the proportion of children dispensed a single drug decreased (slope -.02%, p = .001). Overall, boys had higher monthly rates than girls (average difference 0.9%, p = .002). However, differences by gender were greatest during middle childhood, especially for respiratory and central nervous system agents. Contraceptives accounted for a large proportion of dispensed medication to older teenage girls. Rates for the drugs with the highest prevalence in this study were moderately correlated (average Pearson r.66) with those from a previously published national survey. Conclusion: On average, nearly one quarter of a population of insured children in the United States was dispensed medication each month. This rate decreased somewhat over time, primarily because proportionally fewer children were dispensed a single medication. The rate for two or more drugs dispensed simultaneously remained steady.

Background: Implementing semi-automated processes to efficiently match patients to clinical trials at the point of care requires both detailed patient data and authoritative information about open studies. Objective: To evaluate the utility of the ClinicalTrials.gov registry as a data source for semi-automated trial eligibility screening. Methods: Eligibility criteria and metadata for 437 trials open for recruitment in four different clinical domains were identified in ClinicalTrials.gov. Trials were evaluated for up to date recruitment status and eligibility criteria were evaluated for obstacles to automated interpretation. Finally, phone or email outreach to coordinators at a subset of the trials was made to assess the accuracy of contact details and recruitment status. Results: 24% (104 of 437) of trials declaring on open recruitment status list a study completion date in the past, indicating out of date records. Substantial barriers to automated eligibility interpretation in free form text are present in 81% to up to 94% of all trials. We were unable to contact coordinators at 31% (45 of 146) of the trials in the subset, either by phone or by email. Only 53% (74 of 146) would confirm that they were still recruiting patients. Conclusion: Because ClinicalTrials.gov has entries on most US and many international trials, the registry could be repurposed as a comprehensive trial matching data source. Semi-automated point of care recruitment would be facilitated by matching the registry's eligibility criteria against clinical data from electronic health records. But the current entries fall short. Ultimately, improved techniques in natural language processing will facilitate semi-automated complex matching. As immediate next steps, we recommend augmenting ClinicalTrials.gov data entry forms to capture key eligibility criteria in a simple, structured format.

We have developed an interface to serve patient data from Informatics for Integrating Biology and the Bedside (i2b2) repositories in the Fast Healthcare Interoperability Resources (FHIR) format, referred to as a SMART-on-FHIR cell. The cell serves FHIR resources on a per-patient basis, and supports the “substitutable” modular third-party applications (SMART) OAuth2 specification for authorization of client applications. It is implemented as an i2b2 server plug-in, consisting of 6 modules: authentication, REST, i2b2-to-FHIR converter, resource enrichment, query engine, and cache. The source code is freely available as open source. We tested the cell by accessing resources from a test i2b2 installation, demonstrating that a SMART app can be launched from the cell that accesses patient data stored in i2b2. We successfully retrieved demographics, medications, labs, and diagnoses for test patients. The SMART-on-FHIR cell will enable i2b2 sites to provide simplified but secure data access in FHIR format, and will spur innovation and interoperability. Further, it transforms i2b2 into an apps platform.

Background and objective Upgrades to electronic health record (EHR) systems scheduled to be introduced in the USA in 2014 will advance document interoperability between care providers. Specifically, the second stage of the federal incentive program for EHR adoption, known as Meaningful Use, requires use of the Consolidated Clinical Document Architecture (C-CDA) for document exchange. In an effort to examine and improve C-CDA based exchange, the SMART (Substitutable Medical Applications and Reusable Technology) C-CDA Collaborative brought together a group of certified EHR and other health information technology vendors. Materials and methods We examined the machine-readable content of collected samples for semantic correctness and consistency. This included parsing with the open-source BlueButton.js tool, testing with a validator used in EHR certification, scoring with an automated open-source tool, and manual inspection. We also conducted group and individual review sessions with participating vendors to understand their interpretation of C-CDA specifications and requirements. Results: We contacted 107 health information technology organizations and collected 91 C-CDA sample documents from 21 distinct technologies. Manual and automated document inspection led to 615 observations of errors and data expression variation across represented technologies. Based upon our analysis and vendor discussions, we identified 11 specific areas that represent relevant barriers to the interoperability of C-CDA documents. Conclusions: We identified errors and permissible heterogeneity in C-CDA documents that will limit semantic interoperability. Our findings also point to several practical opportunities to improve C-CDA document quality and exchange in the coming years.

Pediatric populations continue to be understudied in clinical drug trials despite the increasing use of pharmacotherapy in children, particularly with psychotropic drugs. Most pertinent to the clinical selection of drug interventions are trials directly comparing drugs against other drugs. The aim was to measure the prevalence of active drug comparators in neuropsychiatric drug trials in children and identify the effects of funding source on comparator selection. We analyzed the selection of drugs and drug comparisons in clinical trials registered between January 2006 and May 2012. Completed and ongoing interventional trials examining treatments for six neuropsychiatric conditions in children were included. Networks of drug comparisons for each condition were constructed using information about the trial study arms. Of 421 eligible trial registrations, 228 (63,699 participants) were drug trials addressing ADHD (106 trials), autism spectrum disorders (47), unipolar depression (16), seizure disorders (38), migraines and other headaches (15), or schizophrenia (11). Active drug comparators were used in only 11.0% of drug trials while 44.7% used a placebo control and 44.3% no drug or placebo comparator. Even among conditions with well-established pharmacotherapeutic options, almost all drug interventions were compared to a placebo. Active comparisons were more common among trials without industry funding (17% vs. 8%, p=0.04). Trials with industry funding differed from non-industry trials in terms of the drugs studied and the comparators selected. For 73% (61/84) of drugs and 90% (19/21) of unique comparisons, trials were funded exclusively by either industry or non-industry. We found that industry and non-industry differed when choosing comparators and active drug comparators were rare for both groups. This gap in pediatric research activity limits the evidence available to clinicians treating children and suggests a need to reassess the design and funding of pediatric trials in order to optimize the information derived from pediatric participation in clinical trials.