Some experiences and opportunities for big data in translational research.

Health care has become increasingly information intensive. The advent of genomic data, integrated into patient care, significantly accelerates the complexity and amount of clinical data. Translational research in the present day increasingly embraces new biomedical discovery in this data-intensive world, thus entering the domain of "big data." The Electronic Medical Records and Genomics consortium has taught us many lessons, while simultaneously advances in commodity computing methods enable the academic community to affordably manage and process big data. Although great promise can emerge from the adoption of big data methods and philosophy, the heterogeneity and complexity of clinical data, in particular, pose additional challenges for big data inferencing and clinical application. However, the ultimate comparability and consistency of heterogeneous clinical information sources can be enhanced by existing and emerging data standards, which promise to bring order to clinical data chaos. Meaningful Use data standards in particular have already simplified the task of identifying clinical phenotyping patterns in electronic health records.

FHH Quick App Review: How Can a Quality Review Process Assist Primary Care Providers in Choosing a Family Health History App for Patient Care?

Family health history (FHH) is a data type serving risk assessment, diagnosis, research, and preventive health. Despite technological leaps in genomic variant detection, FHH remains the most accessible, least expensive, and most practical assessment tool for assessing risks attributable to genetic inheritance. The purpose of this manuscript is to outline a process to assist primary care professionals in choosing FHH digital tools for patient care based on the new ISO/TS 82304-2 Technical Specification (TS), which is a recently developed method to determine eHealth app quality. With a focus on eHealth in primary care, we applied the quality label concept to FHH, and how a primary care physician can quickly review the quality and reliability of an FHH app. Based on our review of the ISO TS's 81 questions, we compiled a list of 25 questions that are recommended to be more succinct as an initial review. We call this process the FHH Quick App Review. Our 'informative-only' 25 questions do not produce a quality score, but a guide to complete an initial review of FHH apps. Most of the questions are straight from the ISO TS, some are modified or de novo. We believe the 25 questions are not only relevant to FHH app reviews but could also serve to aid app development and clinical implementation.

Deriving consumer-facing disease concepts for family health histories using multi-source sampling.

The family health history has long been recognized as an effective way of understanding individuals' susceptibility to familial disease; yet electronic tools to support the capture and use of these data have been characterized as inadequate. As part of an ongoing effort to build patient-facing tools for entering detailed family health histories, we have compiled a set of concepts specific to familial disease using multi-source sampling. These concepts were abstracted by analyzing family health history data patterns in our enterprise data warehouse, collection patterns of consumer personal health records, analyses from the local state health department, a healthcare data dictionary, and concepts derived from genetic-oriented consumer education materials. Collectively, these sources yielded a set of more than 500 unique disease concepts, represented by more than 2500 synonyms for supporting patients in entering coded family health histories. We expect that these concepts will be useful in providing meaningful data and education resources for patients and providers alike.

Profiling Fast Healthcare Interoperability Resources (FHIR) of Family Health History based on the Clinical Element Models.

In this study we developed a Fast Healthcare Interoperability Resources (FHIR) profile to support exchanging a full pedigree based family health history (FHH) information across multiple systems and applications used by clinicians, patients, and researchers. We used previously developed clinical element models (CEMs) that are capable of representing the FHH information, and derived essential data elements including attributes, constraints, and value sets. We analyzed gaps between the FHH CEM elements and existing FHIR resources. Based on the analysis, we developed a profile that consists of 1) FHIR resources for essential FHH data elements, 2) extensions for additional elements that were not covered by the resources, and 3) a structured definition to integrate patient and family member information in a FHIR message. We implemented the profile using an open-source based FHIR framework and validated it using patient-entered FHH data that was captured through a locally developed FHH tool.

Hemangioma is associated with atopic disease.

OBJECTIVE: To determine if atopic disease is associated with infantile hemangioma. STUDY DESIGN: Case control study. SETTING: State of Utah inpatient and outpatient records obtained from the Department of Health, Intermountain Healthcare medical records, and the University of Utah Health Care medical records using the Utah Population Database. SUBJECTS: Patients given an International Classification of Diseases, Ninth Revision diagnosis of hemangioma prior to age 5 years, from 1991 to 2009. METHODS: Allergy, asthma, and eczema rates in the hemangioma cohort compared to randomly matched controls using logistic regression analysis. RESULTS: The authors identified 2063 patients in the hemangioma group. The overall odds ratio of atopic disease in patients diagnosed with hemangioma was 1.67 compared to the control group (P < .0001). In the hemangioma cohort, the authors found a 36% increased risk of allergies (P < .0001), 67% increased risk of asthma (P < 4e-12), and 82% increased risk of eczema (P < 2e-16). CONCLUSION: This study indicates that allergy, asthma, and eczema are positively associated with hemangioma. Eczema was most strongly associated with hemangioma, with a nearly 2-fold increased risk. Understanding the relationship between atopic disease and infantile hemangioma may elucidate the pathophysiology of each and ultimately lead to better treatment options.

Familial clustering of hemangiomas.

OBJECTIVES: To assess the degree of relationship among individuals with hemangiomas and to evaluate the relative risk (RR) for family members of individuals with hemangiomas. DESIGN: Retrospective case-control study. SETTING: Utah Population Database. PARTICIPANTS: Data sets of individuals of different ages with International Classification of Diseases, Ninth Revision (ICD-9) codes for hemangiomas were created from sources having medical records linked to the Utah Population Database. Controls were selected who matched cases for sex, birth year, and birthplace inside vs outside of Utah. Ten controls were selected per case, and sampling was performed without replacement. Kinship analysis tools were used to identify pedigrees having excess individuals with hemangiomas. MAIN OUTCOME MEASURE: Using conditional logistic regression analysis, RR for hemangiomas among several kinship classes was determined. RESULTS: Identified were 2514 distinct cases 12 years or younger with ICD-9 code 228.01, and the RR for sibs in this group was significantly increased (RR, 2.52; P < .001). Seventy-three founder families had 5 or more affected descendants with cluster P values ≤ .01; familial standardized incidence ratios ranged from 1.64 to 9.50. Family sizes ranged from 546 to 22 291 descendants. CONCLUSIONS: Sibs have increased RR for infantile hemangiomas, suggesting a potential genetic contribution to this likely multifactorial disease. Identification of large families with distantly related individuals will be helpful for future shared segment identification analyses.

Development and early usage patterns of a consumer-facing family health history tool.

Personalized medicine will require detailed clinical patient profiles, and a particular focus on capturing data that is useful in forecasting risk. A detailed family health history is considered a critical component of these profiles, insomuch that it has been coined as 'the best genetic test available'. Despite this, tools aimed at capturing this information for use in electronic health records have been characterized as inadequate. In this manuscript we detail the creation of a patient-facing family health history tool known as OurFamilyHealth, whose long-term emphasis is to facilitate risk assessment and clinical decision support. We present the rationale for such a tool, describe its development and release as a component of Intermountain Healthcare's patient portal, and detail early usage statistics surrounding the application. Data derived from the tool since its release are also compared against family history charting patterns in Intermountain's electronic health records, revealing differences in data availability.

Ideal features for a patient-entered family history and risk assessment tool.

Family history (FH) is an important risk factor for many diseases and its assessment can be a powerful tool for identifying and stratifying patients at risk. As part of an initiative to improve FH collection and decision support at Intermountain Healthcare we conducted a literature review on FH data collection and reviewed a number of current Web-based tools. An ideal list of features was produced to help implement goals of the initiative.

Integrating genetic information resources with an EHR.

As the knowledge about the genetic factors associated with clinical conditions increases, access to information that can help practicing clinicians better understand these factors becomes essential for optimal care and communication with patients. We describe the implementation of 'infobuttons' from the problem list module of an electronic health record (EHR) to on-line genetic resources.