An iOS Framework for the Indivo X Personally Controlled Health Record

The Indivo X personally controlled health record creates a channel between researchers and the patient/subject in several large scale projects. Indivo enables patients to access their health data through a web interface and, as an “apps platform”, can be extended in functionality. Patient-facing apps, such as a medication list, may improve the data flow between researcher and patient, in both directions, and as such provide better data for the researcher and immediate benefit for the patient. However, research projects in general do not allocate large funds to patient facing apps, let alone a mobile interface. Thus we have created a framework that greatly simplifies connecting an iOS app to an Indivo X server. Our open-source framework enables novel as well as experienced iOS developers to build mobile interfaces for their research subjects, taking advantage of Indivo X

ClinicalTrials.gov as a Data Source for Semi-Automated Point-Of-Care Trial Eligibility Screening

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24% (104 of 437) of trials declaring on open recruitment status list a study completion date in the past, indicating out of date records.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

C3-PRO: Connecting ResearchKit to the Health System Using i2b2 and FHIR

A renewed interest by consumer information technology giants in the healthcare domain is focused on transforming smartphones into personal health data storage devices. With the introduction of the open source ResearchKit, Apple provides a framework for researchers to inform and consent research subjects, and to readily collect personal health data and patient reported outcomes (PRO) from distributed populations. However, being research backend agnostic, ResearchKit does not provide data transmission facilities, leaving research apps disconnected from the health system. Personal health data and PROs are of the most value when presented in context along with health system data. Our aim was to build a toolchain that allows easy and secure integration of personal health and PRO data into an open source platform widely adopted across 140 academic medical centers. We present C3-PRO: the Consent, Contact, and Community framework for Patient Reported Outcomes. This open source toolchain connects, in a standards-compliant fashion, any ResearchKit app to the widely-used clinical research infrastructure Informatics for Integrating Biology and the Bedside (i2b2). C3-PRO leverages the emerging health data standard Fast Healthcare Interoperability Resources (FHIR)

Screenshots of the C Tracker app making use of C3-PRO.

<p><b>A</b>) App dashboard for participation overview. <b>B</b>) Viewing the generated consent PDF file, including signature and date, on device. <b>C</b>) Filling out a short survey, showing one of the questions rendered by ResearchKit.</p

OAuth2 dynamic client registration flow, extended to use the app's App Store receipt.

<p>The receipt data is sent alongside the standard OAuth2 registration parameters and verified with Apple's iTunes servers. If the receipt is valid, standard dynamic client registration continues. Subsequently the app can request access tokens with the supplied client key and -secret through an OAuth2 "client credentials” flow.</p

Effective recruitment status.

<p>A) Recruitment status (x-axis), verified by phone or email, of the trials in our 4 trial sets. B) The same recruitment status compared against how long ago the trials were last updated on ClinicalTrials.gov. Trials closed for recruitment tended not to have been updated recently when compared to trials open for recruitment (p<0.001).</p

Comparison of the time since last update to whether the recruitment status is conflicting with the stated study completion date.

<p>Trials with a recruitment status conflicting with their stated completion date tended to not have been updated recently (p<0.001).</p

Number of trials and eligibility criteria.

<p>Number of trials and individual eligibility criteria in our 4 data sets and the date of data retrieval from ClinicalTrials.gov.</p><p>Number of trials and eligibility criteria.</p

Computationally challenging elgibility criteria.

<p>The percentage of trials, broken down per trial set, that include at least one criterion only applying to a sub-population (sub-population) of the targeted patient cohort, that contain at least one laboratory value or medical score (labs and scores), that have at least one criterion that is temporally constrained (temporal), that have at least one criterion describing patient behavior or abilities (patient) and that have at least one of these four criteria (any).</p><p>Computationally challenging elgibility criteria.</p

C3-PRO data flow.

<p>Data flow from data capture on the phone through the C3-PRO receiver, consumer and i2b2 cell into the i2b2 database.</p