An 'Honest Broker' mechanism to maintain privacy for patient care and academic medical research

Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study

BACKGROUND

Mood disorder has emerged as a serious concern for public health; in particular, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early prediction is challenging due to overlapping symptoms. Recently, there have been attempts to develop a prediction model by using federated learning. Federated learning in medical fields is a method for training multi-institutional machine learning models without patient-level data sharing.

OBJECTIVE

This study aims to develop and validate a federated, differentially private multi-institutional bipolar transition prediction model.

METHODS

This retrospective study enrolled patients diagnosed with the first depressive episode at 5 tertiary hospitals in South Korea. We developed models for predicting bipolar transition by using data from 17,631 patients in 4 institutions. Further, we used data from 4541 patients for external validation from 1 institution. We created standardized pipelines to extract large-scale clinical features from the 4 institutions without any code modification. Moreover, we performed feature selection in a federated environment for computational efficiency and applied differential privacy to gradient updates. Finally, we compared the federated and the 4 local models developed with each hospital's data on internal and external validation data sets.

RESULTS

In the internal data set, 279 out of 17,631 patients showed bipolar disorder transition. In the external data set, 39 out of 4541 patients showed bipolar disorder transition. The average performance of the federated model in the internal test (area under the curve [AUC] 0.726) and external validation (AUC 0.719) data sets was higher than that of the other locally developed models (AUC 0.642-0.707 and AUC 0.642-0.699, respectively). In the federated model, classifications were driven by several predictors such as the Charlson index (low scores were associated with bipolar transition, which may be due to younger age), severe depression, anxiolytics, young age, and visiting months (the bipolar transition was associated with seasonality, especially during the spring and summer months).

CONCLUSIONS

We developed and validated a differentially private federated model by using distributed multi-institutional psychiatric data with standardized pipelines in a real-world environment. The federated model performed better than models using local data only.

Patient and researcher stakeholder preferences for use of electronic health record data: a qualitative study to guide the design and development of a platform to honor patient preferences

OBJECTIVE

This qualitative study aimed to understand patient and researcher perspectives regarding consent and data-sharing preferences for research and a patient-centered system to manage consent and data-sharing preferences.

MATERIALS AND METHODS

We conducted focus groups with patient and researcher participants recruited from three academic health centers via snowball sampling. Discussions focused on perspectives on the use of electronic health record (EHR) data for research. Themes were identified through consensus coding, starting from an exploratory framework.

RESULTS

We held two focus groups with patients (n = 12 patients) and two with researchers (n = 8 researchers). We identified two patient themes (1-2), one theme common to patients and researchers (3), and two researcher themes (4-5). Themes included (1) motivations for sharing EHR data, (2) perspectives on the importance of data-sharing transparency, (3) individual control of personal EHR data sharing, (4) how EHR data benefits research, and (5) challenges researchers face using EHR data.

DISCUSSION

Patients expressed a tension between the benefits of their data being used in studies to benefit themselves/others and avoiding risk by limiting data access. Patients resolved this tension by acknowledging they would often share their data but wanted greater transparency on its use. Researchers expressed concern about incorporating bias into datasets if patients opted out.

CONCLUSIONS

A research consent and data-sharing platform must consider two competing goals: empowering patients to have more control over their data and maintaining the integrity of secondary data sources. Health systems and researchers should increase trust-building efforts with patients to engender trust in data access and use.

Data Sharing of Imaging in an Evolving Health Care World: Report of the ACR Data Sharing Workgroup Part 2: Annotation, Curation, and Contracting

A core principle of ethical data sharing is maintaining the security and anonymity of the data, and care must be taken to ensure medical records and images cannot be reidentified to be traced back to patients or misconstrued as a breach in the trust between health care providers and patients. Once those principles have been observed, those seeking to share data must take the appropriate steps to curate the data in a way that organizes the clinically relevant information so as to be useful to the data sharing party, assesses the ensuing value of the data set and its annotations, and informs the data sharing contracts that will govern use of the data. Embarking on a data sharing partnership engenders a host of ethical, practical, technical, legal, and commercial challenges that require a thoughtful, considered approach. In 2019 the ACR convened a Data Sharing Workgroup to develop philosophies around best practices in the sharing of health information. This is Part 2 of a Report on the workgroup's efforts in exploring these issues.

ACE: the Advanced Cohort Engine for searching longitudinal patient records

OBJECTIVE

To propose a paradigm for a scalable time-aware clinical data search, and to describe the design, implementation and use of a search engine realizing this paradigm.

MATERIALS AND METHODS

The Advanced Cohort Engine (ACE) uses a temporal query language and in-memory datastore of patient objects to provide a fast, scalable, and expressive time-aware search. ACE accepts data in the Observational Medicine Outcomes Partnership Common Data Model, and is configurable to balance performance with compute cost. ACE's temporal query language supports automatic query expansion using clinical knowledge graphs. The ACE API can be used with R, Python, Java, HTTP, and a Web UI.

RESULTS

ACE offers an expressive query language for complex temporal search across many clinical data types with multiple output options. ACE enables electronic phenotyping and cohort-building with subsecond response times in searching the data of millions of patients for a variety of use cases.

DISCUSSION

ACE enables fast, time-aware search using a patient object-centric datastore, thereby overcoming many technical and design shortcomings of relational algebra-based querying. Integrating electronic phenotype development with cohort-building enables a variety of high-value uses for a learning health system. Tradeoffs include the need to learn a new query language and the technical setup burden.

CONCLUSION

ACE is a tool that combines a unique query language for time-aware search of longitudinal patient records with a patient object datastore for rapid electronic phenotyping, cohort extraction, and exploratory data analyses.

Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data

Background

The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user’s data on that user’s device. By keeping data on users’ devices, federated learning protects users’ private data from data leaks and breaches on the researcher’s central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain.

Objective

We review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models.

Methods

We applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject.

Results

In a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average.

Conclusions

Our findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods.

Personalized Medicine and the Power of Electronic Health Records

Personalized medicine has largely been enabled by the integration of genomic and other data with electronic health records (EHRs) in the United States and elsewhere. Increased EHR adoption across various clinical settings and the establishment of EHR-linked population-based biobanks provide unprecedented opportunities for the types of translational and implementation research that drive personalized medicine. We review advances in the digitization of health information and the proliferation of genomic research in health systems and provide insights into emerging paths for the widespread implementation of personalized medicine.

Intraoperative hyperglycemia is independently associated with infectious complications after non-cardiac surgery

Background

Perioperative hyperglycemia and its associated increase in morbidity and mortality have been well studied in the critical care and cardiac surgery literature. However, there is little data regarding the impact of intraoperative hyperglycemia on post-operative infectious complications in non-cardiac surgery.

Methods

All National Surgery Quality Improvement Program patients undergoing general, vascular, and urological surgery at our tertiary care center were reviewed. After integrating intraoperative glucose measurements from our intraoperative electronic health record, we categorized patients as experiencing mild (8.3–11.0 mmol/L), moderate (11.1–16.6 mmol/L), and severe (≥ 16.7 mmol/L) intraoperative hyperglycemia. Using multiple logistic regression to adjust for patient comorbidities and surgical factors, we evaluated the association of hyperglycemia with the primary outcome of postoperative surgical site infection, pneumonia, urinary tract infection, or sepsis within 30 days.

Results

Of 13,954 patients reviewed, 3150 patients met inclusion criteria and had an intraoperative glucose measurement. 49% (n = 1531) of patients experienced hyperglycemia and 15% (n = 482) patients experienced an infectious complication. Patients with mild (adjusted odds ratio 1.30, 95% confidence interval [1.01 to 1.68], p-value = 0.04) and moderate hyperglycemia (adjusted odds ratio 1.57, 95% confidence interval [1.08–2.28], p-value = 0.02) had a statistically significant risk-adjusted increase in infectious complications. The model c-statistic was 0.72 [95% confidence interval 0.69–0.74].

Conclusions

This is one of the first studies to demonstrate an independent relationship between intraoperative hyperglycemia and postoperative infectious complications. Future studies are needed to evaluate a causal relationship and impact of treatment.

Electronic supplementary material

The online version of this article (10.1186/s12871-018-0546-0) contains supplementary material, which is available to authorized users.

Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned

Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating 'big data' across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.

Biobanking-Budgets and the Role of Pathology Biobanks in Precision Medicine

Biobanks have become an important component of the routine practice of pathology. At the 2016 meeting of the Association of Pathology Chairs, a series of presentations covered several important aspects of biobanking. An often overlooked aspect of biobanking is the fiscal considerations. A biobank budget must address the costs of consenting, procuring, processing, and preserving high-quality biospecimens. Multiple revenue streams will frequently be necessary to create a sustainable biobank; partnering with other key stakeholders has been shown to be successful at academic institutions which may serve as a model. Biobanking needs to be a deeply science-driven and innovating process so that specimens help transform patient-centered clinical and basic research (ie, fulfill the promise of precision medicine). Pathology’s role must be at the center of the biobanking process. This ensures that optimal research samples are collected while guaranteeing that clinical diagnostics are never impaired. Biobanks will continue to grow as important components in the mission of pathology, especially in the era of precision medicine.

Using a computer simulation for teaching communication skills: A blinded multisite mixed methods randomized controlled trial

OBJECTIVES

To assess advanced communication skills among second-year medical students exposed either to a computer simulation (MPathic-VR) featuring virtual humans, or to a multimedia computer-based learning module, and to understand each group's experiences and learning preferences.

METHODS

A single-blinded, mixed methods, randomized, multisite trial compared MPathic-VR (N=210) to computer-based learning (N=211). Primary outcomes: communication scores during repeat interactions with MPathic-VR's intercultural and interprofessional communication scenarios and scores on a subsequent advanced communication skills objective structured clinical examination (OSCE). Multivariate analysis of variance was used to compare outcomes.

SECONDARY OUTCOMES

student attitude surveys and qualitative assessments of their experiences with MPathic-VR or computer-based learning.

RESULTS

MPathic-VR-trained students improved their intercultural and interprofessional communication performance between their first and second interactions with each scenario. They also achieved significantly higher composite scores on the OSCE than computer-based learning-trained students. Attitudes and experiences were more positive among students trained with MPathic-VR, who valued its providing immediate feedback, teaching nonverbal communication skills, and preparing them for emotion-charged patient encounters.

CONCLUSIONS

MPathic-VR was effective in training advanced communication skills and in enabling knowledge transfer into a more realistic clinical situation.

PRACTICE IMPLICATIONS

MPathic-VR's virtual human simulation offers an effective and engaging means of advanced communication training.

Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology

Leading institutions throughout the country have established Precision Medicine programs to support personalized treatment of patients. A cornerstone for these programs is the establishment of enterprise-wide Clinical Data Warehouses. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers, and scientists at Rutgers Cancer Institute of New Jersey have designed, developed, and implemented the Warehouse with information originating from data sources, including Electronic Medical Records, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology and Pathology archives, and Next Generation Sequencing services. Innovative solutions were implemented to detect and extract unstructured clinical information that was embedded in paper/text documents, including synoptic pathology reports. Supporting important precision medicine use cases, the growing Warehouse enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information of patient tumors individually or as part of large cohorts to identify changes and patterns that may influence treatment decisions and potential outcomes.

"Forward-Thinking" in U.S. Biobanking

AIMS

Do biobanks enact policies and plans that allow them to anticipate and respond to potential challenges? If a biobank has one such policy or plan, is it likely to have more? Using survey data from 456 U.S. biobanks, we assess four possible indicators of such "forward-thinking."

METHODS

We present response frequencies and cross-tabulations regarding policies for return of results and ownership of specimens, and for having a formal business plan and a plan for what happens to specimens if the biobank closes. We analyze the relationships among these indicators, using chi-square for tests of statistical significance.

RESULTS

Policies-Sixty-two percent of biobanks have a policy about returning individual research results; 70% have a policy designating ownership of specimens and/or technology. Having these two policies is significantly related (p < 0.001). Plans-34% of biobanks have a formal business plan; 26% have a written plan for what will happen to the specimens if the biobank closes. Having these two plans is significantly related (p < 0.001). Relationships among indicators-only 7% of biobanks are forward-thinking across all four indicators; 12% are forward-thinking across none.

DISCUSSION

The two policies we examined tend to occur together, as do the two plans. These policies and plans seem to tap different aspects of accountability and responsiveness. Specifically, the policies reflect issues most commonly raised in the ethical and legal literature on biobanking, while the plans are indicators of sustainability, a separate area of concern in biobanking.

Novel Method for Recruiting Representative At-Risk Individuals into Cancer Prevention Trials: Online Health Risk Assessment in Employee Wellness Programs

Participation in cancer prevention trials (CPT) is lower than 3 % among high-risk healthy individuals, and racial/ethnic minorities are the most under-represented. Novel recruitment strategies are therefore needed. Online health risk assessment (HRA) serves as a gateway component of nearly all employee wellness programs (EWPs) and may be a missed opportunity. This study aimed to explore employees' interest, willingness, motivators, and barriers of releasing their HRA responses to an external secure research database for recruitment purpose. We used qualitative research methods (focus group and individual interviews) to examine employees' interest and willingness in releasing their online HRA responses to an external, secure database to register as potential CPT participants. Fifteen structured interviews (40 % of study participants were of racial/ethnic minority) were conducted, and responses reached saturation after four interviews. All employees showed interest and willingness to release their online HRA responses to register as a potential CPT participant. Content analyses revealed that 91 % of participants were motivated to do so, and the major motivators were to (1) obtain help in finding personally relevant prevention trials, (2) help people they know who are affected by cancer, and/or (3) increase knowledge about CPT. A subset of participants (45 %) expressed barriers of releasing their HRA responses due to concerns about credibility and security of the external database. Online HRA may be a feasible but underutilized recruitment method for cancer prevention trials. EWP-sponsored HRA shows promise for the development of a large, centralized registry of racially/ethnically representative CPT potential participants.

The biorepository portal toolkit: an honest brokered, modular service oriented software tool set for biospecimen-driven translational research

BACKGROUND

High throughput molecular sequencing and increased biospecimen variety have introduced significant informatics challenges for research biorepository infrastructures. We applied a modular system integration approach to develop an operational biorepository management system. This method enables aggregation of the clinical, specimen and genomic data collected for biorepository resources.

METHODS

We introduce an electronic Honest Broker (eHB) and Biorepository Portal (BRP) open source project that, in tandem, allow for data integration while protecting patient privacy. This modular approach allows data and specimens to be associated with a biorepository subject at any time point asynchronously. This lowers the bar to develop new research projects based on scientific merit without institutional review for a proposal.

RESULTS

By facilitating the automated de-identification of specimen and associated clinical and genomic data we create a future proofed specimen set that can withstand new workflows and be connected to new associated information over time. Thus facilitating collaborative advanced genomic and tissue research.

CONCLUSIONS

As of Janurary of 2016 there are 23 unique protocols/patient cohorts being managed in the Biorepository Portal (BRP). There are over 4000 unique subject records in the electronic honest broker (eHB), over 30,000 specimens accessioned and 8 institutions participating in various biobanking activities using this tool kit. We specifically set out to build rich annotation of biospecimens with longitudinal clinical data; BRP/REDCap integration for multi-institutional repositories; EMR integration; further annotated specimens with genomic data specific to a domain; build application hooks for experiments at the specimen level integrated with analytic software; while protecting privacy per the Office of Civil Rights (OCR) and HIPAA.

Establishing the role of honest broker: bridging the gap between protecting personal health data and clinical research efficiency

Background. The objective of this study is to propose the four conditions for the roles of honest brokers through a review of literature published by ten institutions that are successfully utilizing honest brokers. Furthermore, the study aims to examine whether the Asan Medical Center's (AMC) honest brokers satisfy the four conditions, and examine the need to enhance their roles. Methods. We analyzed the roles, tasks, and types of honest brokers at 10 organizations by reviewing the literature. We also established a Task Force (TF) in our institution for setting the roles and processes of the honest broker system and the honest brokers. The findings of the literature search were compared with the existing systems at AMC-which introduced the honest broker system for the first time in Korea. Results. Only one organization employed an honest broker for validating anonymized clinical data and monitoring the anonymity verifications of the honest broker system. Six organizations complied with HIPAA privacy regulations, while four organizations did not disclose compliance. By comparing functions with those of the AMC, the following four main characteristics of honest brokers were determined: (1) de-identification of clinical data; (2) independence; (3) checking that the data are used only for purposes approved by the IRB; and (4) provision of de-identified data to researchers. These roles were then compared with those of honest brokers at the AMC. Discussion. First, guidelines that regulate the definitions, purposes, roles, and requirements for honest brokers are needed, since there are no currently existing regulations. Second, Korean clinical research institutions and national regulatory departments need to reach a consensus on a Korean version of Limited Data Sets (LDS), since there are no lists that describe the use of personal identification information. Lastly, satisfaction surveys on honest brokers by researchers are necessary to improve the quality of honest brokers.

Building blocks for a clinical imaging informatics environment

Over the past 20 years, imaging informatics has been driven by the widespread adoption of radiology information and picture archiving and communication and speech recognition systems. These three clinical information systems are commonplace and are intuitive to most radiologists as they replicate familiar paper and film workflow. So what is next? There is a surge of innovation in imaging informatics around advanced workflow, search, electronic medical record aggregation, dashboarding, and analytics tools for quality measures (Nance et al., AJR Am J Roentgenol 200:1064-1070, 2013). The challenge lies in not having to rebuild the technological wheel for each of these new applications but instead attempt to share common components through open standards and modern development techniques. The next generation of applications will be built with moving parts that work together to satisfy advanced use cases without replicating databases and without requiring fragile, intense synchronization from clinical systems. The purpose of this paper is to identify building blocks that can position a practice to be able to quickly innovate when addressing clinical, educational, and research-related problems. This paper is the result of identifying common components in the construction of over two dozen clinical informatics projects developed at the University of Maryland Radiology Informatics Research Laboratory. The systems outlined are intended as a mere foundation rather than an exhaustive list of possible extensions.

Privacy preserving interactive record linkage (PPIRL)

OBJECTIVE

Record linkage to integrate uncoordinated databases is critical in biomedical research using Big Data. Balancing privacy protection against the need for high quality record linkage requires a human-machine hybrid system to safely manage uncertainty in the ever changing streams of chaotic Big Data.

METHODS

In the computer science literature, private record linkage is the most published area. It investigates how to apply a known linkage function safely when linking two tables. However, in practice, the linkage function is rarely known. Thus, there are many data linkage centers whose main role is to be the trusted third party to determine the linkage function manually and link data for research via a master population list for a designated region. Recently, a more flexible computerized third-party linkage platform, Secure Decoupled Linkage (SDLink), has been proposed based on: (1) decoupling data via encryption, (2) obfuscation via chaffing (adding fake data) and universe manipulation; and (3) minimum information disclosure via recoding.

RESULTS

We synthesize this literature to formalize a new framework for privacy preserving interactive record linkage (PPIRL) with tractable privacy and utility properties and then analyze the literature using this framework.

CONCLUSIONS

Human-based third-party linkage centers for privacy preserving record linkage are the accepted norm internationally. We find that a computer-based third-party platform that can precisely control the information disclosed at the micro level and allow frequent human interaction during the linkage process, is an effective human-machine hybrid system that significantly improves on the linkage center model both in terms of privacy and utility.

Using cloud-based mobile technology for assessment of competencies among medical students

Valid, direct observation of medical student competency in clinical settings remains challenging and limits the opportunity to promote performance-based student advancement. The rationale for direct observation is to ascertain that students have acquired the core clinical competencies needed to care for patients. Too often student observation results in highly variable evaluations which are skewed by factors other than the student’s actual performance. Among the barriers to effective direct observation and assessment include the lack of effective tools and strategies for assuring that transparent standards are used for judging clinical competency in authentic clinical settings. We developed a web-based content management system under the name, Just in Time Medicine (JIT), to address many of these issues. The goals of JIT were fourfold: First, to create a self-service interface allowing faculty with average computing skills to author customizable content and criterion-based assessment tools displayable on internet enabled devices, including mobile devices; second, to create an assessment and feedback tool capable of capturing learner progress related to hundreds of clinical skills; third, to enable easy access and utilization of these tools by faculty for learner assessment in authentic clinical settings as a means of just in time faculty development; fourth, to create a permanent record of the trainees’ observed skills useful for both learner and program evaluation. From July 2010 through October 2012, we implemented a JIT enabled clinical evaluation exercise (CEX) among 367 third year internal medicine students. Observers (attending physicians and residents) performed CEX assessments using JIT to guide and document their observations, record their time observing and providing feedback to the students, and their overall satisfaction. Inter-rater reliability and validity were assessed with 17 observers who viewed six videotaped student-patient encounters and by measuring the correlation between student CEX scores and their scores on subsequent standardized-patient OSCE exams. A total of 3567 CEXs were completed by 516 observers. The average number of evaluations per student was 9.7 (±1.8 SD) and the average number of CEXs completed per observer was 6.9 (±15.8 SD). Observers spent less than 10 min on 43–50% of the CEXs and 68.6% on feedback sessions. A majority of observers (92%) reported satisfaction with the CEX. Inter-rater reliability was measured at 0.69 among all observers viewing the videotapes and these ratings adequately discriminated competent from non-competent performance. The measured CEX grades correlated with subsequent student performance on an end-of-year OSCE. We conclude that the use of JIT is feasible in capturing discrete clinical performance data with a high degree of user satisfaction. Our embedded checklists had adequate inter-rater reliability and concurrent and predictive validity.

A focus area maturity model for a statewide master person index

OBJECTIVE

The sharing of personally identifiable information across organizational boundaries to facilitate patient identification in Utah presents significant policy challenges. Our objective was to create a focus area maturity model to describe and evaluate our progress in developing a policy framework to support a statewide master person index (sMPI) for healthcare and public health operations and research in Utah.

MATERIALS AND METHODS

We used various artifacts, including minutes from policy guidance committee meetings over a span of 18 months, a report from Utah's Digital Health Services Commission, and a draft technical requirements document to retrospectively analyze our work and create a focus area maturity model describing the domain of policy needed to support the sMPI. We then used our model to assess our progress and future goals.

CONCLUSIONS

The focus area maturity model provides an orderly path that can guide the complex process of developing a functional statewide master person index among diverse, autonomous partners. While this paper focuses on our experience in Utah, we believe that the arguments for using a focus area maturity model to guide the development of state or regional MPIs is of general interest.

A new way to protect privacy in large-scale genome-wide association studies

Motivation: Increased availability of various genotyping techniques has initiated a race for finding genetic markers that can be used in diagnostics and personalized medicine. Although many genetic risk factors are known, key causes of common diseases with complex heritage patterns are still unknown. Identification of such complex traits requires a targeted study over a large collection of data. Ideally, such studies bring together data from many biobanks. However, data aggregation on such a large scale raises many privacy issues.

Results: We show how to conduct such studies without violating privacy of individual donors and without leaking the data to third parties. The presented solution has provable security guarantees.

Contact:jaak.vilo@ut.ee

Supplementary information:Supplementary data are available at Bioinformatics online.

Mobile technology for the facilitation of direct observation and assessment of student performance

BACKGROUND

We developed, implemented, and assessed a web-based clinical evaluation application (i.e., CEX app) for Internet-enabled mobile devices, including mobile phones. The app displays problem-specific checklists that correspond to training problems created by the Clerkship Directors in Internal Medicine.

PURPOSE

We hypothesized that use of the CEX app for directly observing students' clinical skills would be feasible and acceptable, and would demonstrate adequate reliability and validity.

METHODS

Between July 2010 and February 2012, 266 third-year medical students completed 5 to 10 formative CEXs during their internal medicine clerkship. The observers (attendings and residents), who performed the CEX, used the app to guide and document their observations, record their time observing and giving feedback to the students, and their overall satisfaction with the CEX app. Interrater reliability and validity were assessed with 17 observers who viewed 6 videotaped student-patient encounters, and by measuring the correlation between student CEX scores and their scores on subsequent standardized-patient Objective Structured Clinical Examination (OSCE) exams.

RESULTS

A total of 2,523 CEXs were completed by 411 observers. The average number of evaluations per student was 9.8 (± 1.8 SD), and the average number of CEXs completed per observer was 6 (± 11.8 SD). Observers spent less than 10 min on 45.3% of the CEXs and 68.6% of the feedback sessions. An overwhelming majority of observers (90.6%) reported satisfaction with the CEX. Interrater reliability was measured at 0.69 among the observers viewing the videotapes, and their ratings discriminated between competent and noncompetent performances. Student CEX grades, however, did not correlate with their end of 3rd-year OSCE scores.

CONCLUSIONS

The use of this CEX app is feasible and it captures students' clinical performance data with a high rate of user satisfaction. Our embedded checklists had adequate interrater reliability and concurrent validity. The grades measured on this app, however, were not predictive of subsequent student performance.

The evolution of biobanking best practices

Biobanks and biospecimens are critical components for many areas of clinical and basic research. The quality of biospecimens and associated data must be consistent and collected according to standardized methods in order to prevent spurious analytical results that can lead to artifacts being interpreted as valid findings. A number of international institutions have taken the initiative to develop and publish best practices, which include technical recommendations for handling biospecimens as well as recommendations for ethical and regulatory practices in biobanking. These sources of guidance have been useful in raising the overall consistency and quality of research involving biospecimens. However, the lack of international harmonization, uneven adoption, and insufficient oversight of best practices are preventing further improvements in biospecimen quality and coordination among collaborators and biobanking networks. In contrast to the more straightforward technical and management issues, ethical and regulatory practices often involve issues that are more controversial and difficult to standardize.

High mortality of cocaine-related ischemic colitis: a hybrid cohort/case-control study

BACKGROUND

Isolated case reports describe bowel ischemia in cocaine users, and the optimal management of these patients remains uncertain.

DESIGN

Case-control study.

SETTING

Teaching hospitals.

PATIENTS

Patients hospitalized for colonic ischemia related to cocaine compared with noncocaine-related ischemic colitis. Cases were identified by using ICD-9 codes and laboratory urine toxicology tests. Patients were included if they had a confirmed diagnosis of bowel ischemia by CT, colonoscopy, angiography, or, in the case of emergency exploration, a pathology report showing bowel ischemia and a urine toxicology test that was positive for cocaine. Controls were individuals who met the same criteria but had no history of cocaine use and a urine test negative for cocaine. Charts were individually audited for accuracy of coding.

MAIN OUTCOME MEASUREMENTS

Mortality and its risk factors.

RESULTS

Patients with cocaine-related ischemia were significantly younger and had a significantly (P < .05) higher mortality rate than patients with ischemic colitis unrelated to cocaine (cocaine: 5/19 [26%] and noncocaine: 6/78 [7.7%]). The cause of death in all cases was septic shock caused by extensive bowel ischemia. Multivariate logistic regression analysis showed that cocaine-related ischemic colitis was a significant risk factor for mortality (odds ratio 5.77; 95% CI, 1.37-24.39) as was the need for surgical intervention (odds ratio 4.95; 95% CI, 1.22-20.12).

LIMITATIONS

Retrospective design.

CONCLUSIONS

Cocaine-related ischemic colitis has a high mortality. In young patients presenting with acute abdominal pain and/or rectal bleeding with evidence of bowel wall thickening or pneumatosis on imaging studies or colonoscopy, cocaine-related ischemia should be considered. Testing for cocaine use may help identify patients at high risk of sepsis and death.

Enabling comparative effectiveness research with informatics: show me the data!

RATIONALE

Both outcomes researchers and informaticians are concerned with information and data. As such, some of the central challenges to conducting successful comparative effectiveness research can be addressed with informatics solutions.

METHODS

Specific informatics solutions which address how data in comparative effectiveness research are enriched, stored, shared, and analyzed are reviewed.

RESULTS

Imaging data can be made more quantitative, uniform, and structured for researchers through the use of lexicons and structured reporting. Secure and scalable storage of research data is enabled through data warehouses and cloud services. There are a number of national efforts to help researchers share research data and analysis tools.

CONCLUSION

There is a diverse arsenal of informatics tools designed to meet the needs of comparative effective researchers.

Comparative effectiveness research and medical informatics

As is the case for environmental, ecological, astronomical, and other sciences, medical practice and research finds itself in a tsunami of data. This data deluge, due primarily to the introduction of digitalization in routine medical care and medical research, affords the opportunity for improved patient care and scientific discovery. Medical informatics is the subdiscipline of medicine created to make greater use of information in order to improve healthcare. The 4 areas of medical informatics research (information access, structure, analysis, and interaction) are used as a framework to discuss the overlap in information needs of comparative effectiveness research and potential contributions of medical informatics. Examples of progress from the medical informatics literature and the Veterans Affairs Healthcare System are provided.

Custodianship as an ethical framework for biospecimen-based research

Human biological specimens (biospecimens) are increasingly important for research that aims to advance human health. Yet, despite significant proliferation in specimen-based research and discoveries during the past decade, research remains challenged by the inequitable access to high-quality biospecimens that are collected under rigorous ethical standards. This is primarily caused by the complex level of control and ownership exerted by the myriad of stakeholders involved in the biospecimen research process. This article discusses the ethical model of custodianship as a framework for biospecimen-based research to promote fair research access and resolve issues of control and potential conflicts between biobanks, investigators, human research participants (human subjects), and sponsors. Custodianship is the caretaking obligation for biospecimens from initial collection to final dissemination of research findings. It endorses key practices and operating principles for responsible oversight of biospecimens collected for research. Embracing the custodial model would ensure transparency in research, fairness to human research participants, and shared accountability among all stakeholders involved in biospecimen-based research.

Using quantitative and qualitative approaches to understand racial disparities in adult vaccination

BACKGROUND

One proposed explanation for the persistence of racial disparities in adult immunizations is that minority patients receive primary care at practices that differ substantively from practices where white patients receive care. This study used both quantitative and qualitative methods to assess physician and practice factors contributing to disparities in a sample of inner-city, urban, and suburban practices in low to moderate income neighborhoods.

METHODS

Pneumococcal polysaccharide vaccine (PPV) and influenza vaccination rates were determined from medical record review in a sample of 2021 elderly (aged > or = 65 years) patients. Their physicians were surveyed about office systems for adult immunizations and structured observations of practice physical features, and operations were conducted. Case studies of practices with lowest and highest rates and the largest racial disparities are presented.

RESULTS

Overall, weighted PPV vaccination rate was 60%, but rates differed significantly by race (65.8% for whites vs 36.5% for minorities, P < .001 by stratified Cochran-Mantel-Haenszel test). Two of 6 minority panels had PPV rates less than 20%. Overall, weighted influenza vaccination rate, as measured by receipt of the vaccine in 3 of the 5 most recent seasons, was 51.9%, but rates also differed significantly by race (55.6% for whites vs 36.2% for minorities, P < .03, by stratified Cochran-Mantel-Haenszel test).

CONCLUSIONS

Low rates in 2 minority panels, racial disparity between minorities and whites in mixed panels, and between-panel variation in rates contributed to the overall differences in vaccination rates by race.

Understanding adult vaccination in urban, lower-socioeconomic settings: influence of physician and prevention systems

PURPOSE

Vaccination rates for pneumococcal polysaccharide vaccine (PPV) and influenza vaccine are relatively low in disadvantaged urban populations. This study was designed to assess which physician and practice characteristics might explain differences in rates across physicians.

METHODS

PPV and influenza vaccination rates were determined for 2,021 patients aged 65 years and older receiving care from 30 physicians in 17 practices surveyed about their office systems for providing adult immunizations. Hierarchical linear modeling (HLM) analyses were used to examine the relationships among vaccination rates, patient-level characteristics, and physician variables.

RESULTS

Overall, the weighted PPV vaccination rate was 60.0% and varied widely across physicians (range, 11%-98%). At the patient level in HLM, patient race (P=.01) and age (P = .02), but not neighborhood income, were associated with PPV status. By linking physician survey data with PPV rates, we found the best pair of physician variables to be "reported time spent with patients for a well visit" (P = .01) and "use of enhanced immunization documentation" (P=.10). The overall influenza vaccination rate was 51.9% (range, 22%-96%). Patient race (P=.003) and age (P = .002) were associated with influenza vaccination. The pair of physician variables with the strongest association with influenza vaccination was "use of standing orders" (P <.001) and "average observed physician examination room time," regardless of visit type (P=.02).

CONCLUSIONS

Vaccination rates vary widely in urban settings and are associated with practice characteristics such as time spent with patients and, for influenza vaccine, use of standing orders.

The University of Michigan Honest Broker: a Web-based service for clinical and translational research and practice

For the success of clinical and translational science, a seamless interoperation is required between clinical and research information technology. Addressing this need, the Michigan Clinical Research Collaboratory (MCRC) was created. The MCRC employed a standards-driven Web Services architecture to create the U-M Honest Broker, which enabled sharing of clinical and research data among medical disciplines and separate institutions. Design objectives were to facilitate sharing of data, maintain a master patient index (MPI), deidentification of data, and routing data to preauthorized destination systems for use in clinical care, research, or both. This article describes the architecture and design of the U-M HB system and the successful demonstration project. Seventy percent of eligible patients were recruited for a prospective study examining the correlation between interventional cardiac catheterizations and depression. The U-M Honest Broker delivered on the promise of using structured clinical knowledge shared among providers to help clinical and translational research.

Mobile healthcare services in school-based health center

OBJECTIVES

In Taiwan, campus health problems are placing more and more pressure on school-based health centers (SBHCs). Moreover, SBHCs have insufficient resources to actively provide follow-up healthcare for students and faculty found to be overweight, chronically ill, or at high risk. In order to improve the quality of SBHC healthcare, a project was begun to enhance the efficiency of campus healthcare services by employing web-based and cell phone-based services. This project employed the Mobile Automated Medical Alert (MAMA) system, which was designed especially for campus health center use. Before implementing the MAMA system, a focus group of healthcare related staff identified areas in which SBHC healthcare services might be improved by the system and created a questionnaire to measure student and faculty response to the proposed services. Modifications to the MAMA design and service offerings were made based upon these questionnaire results.

METHOD

After the initial brainstorming session, the MAMA team created a survey instrument that was administered to students and faculty in order to understand their attitudes toward the proposed mobile healthcare services. Two hundred questionnaires were distributed to students and faculty at a private technical college in central Taiwan.

RESULTS

Among the respondents, 100% had cellular phones and used short-text messages. Ninety-five percent of the student respondents and 85% of the faculty respondents agreed that mobile healthcare system would improve the quality of the health care their SBHC was currently providing. Based on these positive survey responses, the MAMA system was implemented.

DISCUSSION/CONCLUSION

The current paper describes the process by which the MAMA team brainstormed proposed services and administered their survey. Survey results and the consequent changes to the MAMA system are discussed. Finally, recommendations are made for MAMA system use and a description is offered of the impact such systems might have on the future of college campus healthcare services.

A multidisciplinary approach to honest broker services for tissue banks and clinical data: a pragmatic and practical model

BACKGROUND

Honest broker services are essential for tissue- and data-based research. The honest broker provides a firewall between clinical and research activities. Clinical information is stripped of Health Insurance Portability and Accountability Act-denoted personal health identifiers. Research material may have linkage codes, precluding the identification of patients to researchers. The honest broker provides data derived from clinical and research sources. These data are for research use only, and there are rules in place that prohibit reidentification. Very rarely, the institutional review board (IRB) may allow recontact and develop a recontact plan with the honest broker. Certain databases are structured to serve a clinical and research function and incorporate 'real-time' updating of information. This complex process needs resolution of a variety of issues regarding the precise role of the HB and their interaction with data. There also is an obvious need for software solutions to make the task of deidentification easier.

METHODS

The University of Pittsburgh has implemented a novel, IRB-approved mechanism to address honest broker functions to meet the specimen and data needs of researchers. The Tissue Bank stores biologic specimens. The Cancer Registry culls data and annotating information as part of state- and federal-mandated functions and collects data on the clinical progression, treatment, and outcomes of cancer patients. The Cancer Registry also has additional IRB approval to collect data elements only for research purposes. The Clinical Outcomes Group is involved in patient safety and health services research. Radiation Oncology and Medical Oncology provide critical treatment related information. Pathology and Oncology Informatics have designed software tools for querying availability of specimens, extracting data, and deidentifying specimens and annotating data for clinical and translational research. These entities partnered and submitted a joint IRB proposal to create an institutional honest broker facility. The employees of this conglomerate have honest broker agreements with the University of Pittsburgh and the Medical Center. This provides a large group of honest brokers, ensuring availability for projects without any conflict of interest.

RESULTS

The honest broker system has been an IRB-approved institutional entity at the University of Pittsburgh since 2003. The honest broker system currently includes 33 certified honest brokers encompassing the multiple partners of this system. The honest broker system has handled >1600 requests over the past 4 years with a 25% increase in volume each year.

CONCLUSIONS

The current results indicate that the collaborative honest broker model described herein is robust and provides a highly functional solution to the specimen and data needs for critical clinical and translational research activities.

Assessing disparities in adult vaccination using multimodal approaches in primary care offices: methodology

Racial disparities in invasive pneumococcal disease and pneumococcal polysaccharide vaccination (PPV) persist despite significant progress. One reason may be that minority patients receive primary care at practices with fewer resources, less efficient office systems, and different priorities. The purposes of this paper are: (1) to describe the recruitment of a diverse array of primary care practices in Pittsburgh, Pennsylvania serving white and minority patient populations, and the multimodal data collection process that included surveys of key office personnel, observations of practice operations and medical record reviews for determining PPV vaccination rates; and (2) to report the results of the sampling strategy. During 2005, 18 practices participated in the study, six with a predominantly minority patient population, nine with a predominantly white patient population, and three with a racial distribution similar to that of this locality. Eight were solo practices and 10 were multiprovider practices; they included federally qualified health centers, privately owned practices and faculty and University of Pittsburgh Medical Center community practices. Providers represented several racial and ethnic groups, as did office staffs. PPV rates determined from 2,314 patients' medical records averaged 60.3 +/- 22.6% and ranged from 11% to 97%. Recruitment of practices with attention to location, patient demographics, and provider types results in a diverse sample of practices and patients. Multimodal data collection from these practices should provide a rich data source for examining the complex interplay of factors affecting immunization disparities among older adults.