The challenges of linking health insurer claims with electronic medical records

Beta-blocker initiation under dobutamine infusion in acute advanced heart failure: a target trial emulation with observational data

Aims

In patients with advanced heart failure requiring dobutamine infusion, it is usually recommended to initiate beta-blockers after weaning from dobutamine. However, beta-blockers are sometimes initiated under dobutamine infusion in a real-world scenario. The association between such early beta-blocker initiation with clinical outcomes is unknown. Therefore, this study investigates the association between initiating beta-blockers under dobutamine infusion and survival outcomes.

Methods and results

This observational study with a multicentre inpatient-care database emulated a pragmatic randomized controlled trial (RCT) of the beta-blocker initiation strategy. First, 1151 patients on dobutamine and not on beta-blockers on the day of heart failure admission (Day 0) were identified. Among 1095 who met eligibility criteria, patients who were eventually initiated beta-blockers under dobutamine infusion by Day 7 (early initiation strategy) were 1:1 matched to those who were not initiated (conservative strategy). The methods of cloning, censoring, and weighting were applied to emulate the target trial. Patients were followed up for up to 30 days. The primary outcome was all-cause death. Among 780 matched patients (median age, 81 years), the adjusted hazard ratio was 1.11 (95% confidence interval 0.75-1.64, P = 0.59) for the early initiation strategy. The estimated 30-day all-cause mortalities in the early initiation strategy and the conservative strategy were 19.3% (10.6-30.7) and 16.2% (9.2-25.3), respectively. The results were consistent when we used different days to determine strategies (i.e. 5 and 9) instead of 7 days.

Conclusion

The present observational study emulating a pragmatic RCT found no positive or negative association between beta-blocker initiation under dobutamine infusion and overall survival.

Virtual patient identifier (vPID): Improving patient traceability using anonymized identifiers in Japanese healthcare insurance claims database

Objective

Japan's national-level healthcare insurance claims database (NDB) is a collective database that contains the entire information on healthcare services being provided to all citizens. However, existing anonymized identifiers (ID1 and ID2) have a poor capability of tracing patients' claims in the database, hindering longitudinal analyses. This study presents a virtual patient identifier (vPID), which we have developed on top of these existing identifiers, to improve the patient traceability.

Methods

vPID is a new composite identifier that intensively consolidates ID1 and ID2 co-occurring in an identical claim to allow to collect claims of each patient even though its ID1 or ID2 may change due to life events or clerical errors. We conducted a verification test with prefecture-level datasets of healthcare insurance claims and enrollee history records, which allowed us to compare vPID with the ground truth, in terms of an identifiability score (indicating a capability of distinguishing a patient's claims from another patient's claims) and a traceability score (indicating a capability of collecting claims of an identical patient).

Results

The verification test has clarified that vPID offers significantly higher traceability scores (0.994, Mie; 0.997, Gifu) than ID1 (0.863, Mie; 0.884, Gifu) and ID2 (0.602, Mie; 0.839, Gifu), and comparable (0.996, Mie) and lower (0.979, Gifu) identifiability scores.

Discussion

vPID is seemingly useful for a wide spectrum of analytic studies unless they focus on sensitive cases to the design limitation of vPID, such as patients experiencing marriage and job change, simultaneously, and same-sex twin children.

Conclusion

vPID successfully improves patient traceability, providing an opportunity for longitudinal analyses that used to be practically impossible for NDB. Further exploration is also necessary, in particular, for mitigating identification errors.

Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data

INTRODUCTION

Adverse drug reactions (ADRs) are a leading cause of mortality worldwide and should be detected promptly to reduce health risks to patients. A data-mining approach using large-scale medical records might be a useful method for the early detection of ADRs. Many studies have analyzed medical records to detect ADRs; however, most of them have focused on a narrow range of ADRs, limiting their usefulness.

OBJECTIVE

This study aimed to identify methods for the early detection of a wide range of ADR signals.

METHODS

First, to evaluate the performance in signal detection of ADRs by data-mining, we attempted to create a gold standard based on clinical evidence. Second, association rule mining (ARM) was applied to patient symptoms and medications registered in claims data, followed by evaluating ADR signal detection performance.

RESULTS

We created a new gold standard consisting of 92 positive and 88 negative controls. In the assessment of ARM using claims data, the areas under the receiver-operating characteristic curve and the precision-recall curve were 0.80 and 0.83, respectively. If the detection criteria were defined as lift > 1, conviction > 1, and p-value < 0.05, ARM could identify 156 signals, of which 90 were true positive controls (sensitivity: 0.98, specificity: 0.25). Evaluation of the capability of ARM with short periods of data revealed that ARM could detect a greater number of positive controls than the conventional analysis method.

CONCLUSIONS

ARM of claims data may be effective in the early detection of a wide range of ADR signals.

Real word challenges in integrating electronic medical record and administrative health data for regional quality improvement in diabetes: a retrospective cross-sectional analysis

BACKGROUND

Linked electronic medical records and administrative data have the potential to support a learning health system and data-driven quality improvement. However, data completeness and accuracy must first be assessed before their application. We evaluated the processes, feasibility, and limitations of linking electronic medical records and administrative data for the purpose of quality improvement within five specialist diabetes clinics in Edmonton, Alberta, a province known for its robust health data infrastructure.

METHODS

We conducted a retrospective cross-sectional analysis using electronic medical record and administrative data for individuals ≥ 18 years attending the clinics between March 2017 and December 2018. Descriptive statistics were produced for demographics, service use, diabetes type, and standard diabetes benchmarks. The systematic and iterative process of obtaining results is described.

RESULTS

The process of integrating electronic medical record with administrative data for quality improvement was found to be non-linear and iterative and involved four phases: project planning, information generating, limitations analysis, and action. After limitations analysis, questions were grouped into those that were answerable with confidence, answerable with limitations, and not answerable with available data. Factors contributing to data limitations included inaccurate data entry, coding, collation, migration and synthesis, changes in laboratory reporting, and information not captured in existing databases.

CONCLUSION

Electronic medical records and administrative databases can be powerful tools to establish clinical practice patterns, inform data-driven quality improvement at a regional level, and support a learning health system. However, there are substantial data limitations that must be addressed before these sources can be reliably leveraged.

Integrating Electronic Medical Records and Claims Data for Influenza Vaccine Research

Real-world evidence (RWE) increasingly informs public health and healthcare decisions worldwide. A large database has been created (“Integrated Dataset”) that integrates primary care electronic medical records with pharmacy and medical claims data on >123 million US patients since 2014. This article describes the components of the Integrated Dataset and evaluates its representativeness to the US population and its potential use in evaluating influenza vaccine effectiveness. Representativeness to the US population (2014−2019) was evaluated by comparison with demographic information from the 2019 US census and the National Ambulatory Medical Care Survey (NAMCS). Variables included in the Integrated Dataset were evaluated against World Health Organization (WHO) defined key and non-critical variables for evaluating influenza vaccine performance. The Integrated Dataset contains a variety of information, including demographic data, patient medical history, diagnoses, immunizations, and prescriptions. Distributions of most age categories and sex were comparable with the US Census and NAMCS populations. The Integrated Dataset was less diverse by race and ethnicity. Additionally, WHO key and non-critical variables for the estimation of influenza vaccine effectiveness are available in the Integrated Dataset. In summary, the Integrated Dataset is generally representative of the US population and contains key variables for the assessment of influenza vaccine effectiveness.

Development of eClaim system for private indemnity health insurance in South Korea: Compatibility and interoperability

South Korea has the mandatory National Health Insurance (NHI) and supplemental Private Indemnity Health Insurance (PIHI). According to the Financial Supervisory Service, the share of the population with PIHI increased to 66% in 2018 due to the financial burden. However, since the traditional PIHI claim workflow is based on the paper attachment method, it is a big burden to every stakeholder and limits the usability and accessibility of the claims data. To improve the traditional PIHI claim workflow, we developed the electronic claim (eClaim) service for the PIHI in Korea. We also applied the HL7^® (Health Level Seven) FHIR^® (Fast Healthcare Interoperability Resources) standard to ensure interoperability of the claims data. The proposed eClaim Service has been launched in 2017. It has been increased from 8155 in the first half of 2018 to 114,087 in the second half of 2020. Currently, 60 healthcare providers and 22 payers participated in this service. In this study, we proposed an eClaim workflow and service to improve the legacy system. The proposed method can be helpful to other entities planning for their own health insurance system and also applied to various practical purposes including value-based care, automated claim review, and clinical research.

Data linkages between patient-powered research networks and health plans: a foundation for collaborative research

OBJECTIVE

Patient-powered research networks (PPRNs) are a valuable source of patient-generated information. Diagnosis code-based algorithms developed by PPRNs can be used to query health plans' claims data to identify patients for research opportunities. Our objective was to implement privacy-preserving record linkage processes between PPRN members' and health plan enrollees' data, compare linked and nonlinked members, and measure disease-specific confirmation rates for specific health conditions.

MATERIALS AND METHODS

This descriptive study identified overlapping members from 4 PPRN registries and 14 health plans. Our methods for the anonymous linkage of overlapping members used secure Health Insurance Portability and Accountability Act-compliant, 1-way, cryptographic hash functions. Self-reported diagnoses by PPRN members were compared with claims-based computable phenotypes to calculate confirmation rates across varying durations of health plan coverage.

RESULTS

Data for 21 616 PPRN members were hashed. Of these, 4487 (21%) members were linked, regardless of any expected overlap with the health plans. Linked members were more likely to be female and younger than nonlinked members were. Irrespective of duration of enrollment, the confirmation rates for the breast or ovarian cancer, rheumatoid or psoriatic arthritis or psoriasis, multiple sclerosis, or vasculitis PPRNs were 72%, 50%, 75%, and 67%, increasing to 91%, 67%, 93%, and 80%, respectively, for members with ≥5 years of continuous health plan enrollment.

CONCLUSIONS

This study demonstrated that PPRN membership and health plan data can be successfully linked using privacy-preserving record linkage methodology, and used to confirm self-reported diagnosis. Identifying and confirming self-reported diagnosis of members can expedite patient selection for research opportunities, shorten study recruitment timelines, and optimize costs.

Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis

BACKGROUND

Self-tracking through mobile health technology can augment the electronic health record (EHR) as an additional data source by providing direct patient input. This can be particularly useful in the context of enigmatic diseases and further promote patient engagement.

OBJECTIVES

This study aimed to investigate the additional information that can be gained through direct patient input on poorly understood diseases, beyond what is already documented in the EHR.

METHODS

This was an observational study including two samples with a clinically confirmed endometriosis diagnosis. We analyzed data from 6,925 women with endometriosis using a research app for tracking endometriosis to assess prevalence of self-reported pain problems, between- and within-person variability in pain over time, endometriosis-affected tasks of daily function, and self-management strategies. We analyzed data from 4,389 patients identified through a large metropolitan hospital EHR to compare pain problems with the self-tracking app and to identify unique data elements that can be contributed via patient self-tracking.

RESULTS

Pelvic pain was the most prevalent problem in the self-tracking sample (57.3%), followed by gastrointestinal-related (55.9%) and lower back (49.2%) pain. Unique problems that were captured by self-tracking included pain in ovaries (43.7%) and uterus (37.2%). Pain experience was highly variable both across and within participants over time. Within-person variation accounted for 58% of the total variance in pain scores, and was large in magnitude, based on the ratio of within- to between-person variability (0.92) and the intraclass correlation (0.42). Work was the most affected daily function task (49%), and there was significant within- and between-person variability in self-management effectiveness. Prevalence rates in the EHR were significantly lower, with abdominal pain being the most prevalent (36.5%).

CONCLUSION

For enigmatic diseases, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.

Significant and Distinctive n-Grams in Oncology Notes: A Text-Mining Method to Analyze the Effect of OpenNotes on Clinical Documentation

PURPOSE

OpenNotes is a national movement established in 2010 that gives patients access to their visit notes through online patient portals, and its goal is to improve transparency and communication. To determine whether granting patients access to their medical notes will have a measurable effect on provider behavior, we developed novel methods to quantify changes in the length and frequency of use of n-grams (sets of words used in exact sequence) in the notes.

METHODS

We analyzed 102,135 notes of 36 hematology/oncology clinicians before and after the OpenNotes debut at Beth Israel Deaconess Medical Center. We applied methods to quantify changes in the length and frequency of use of sequential co-occurrence of words (n-grams) in the unstructured content of the notes by unsupervised hierarchical clustering and proportional analysis of n-grams.

RESULTS

The number of significant n-grams averaged over all providers did not change, but for individual providers, there were significant changes. That is, all significant observed changes were provider specific. We identified eight providers who were late note signers. This group significantly reduced its late signing behavior after OpenNotes implementation.

CONCLUSION

Although the number of significant n-grams averaged over all providers did not change, our text-mining method detected major content changes in specific providers' documentation at the n-gram level. The method successfully identified a group of providers who decreased their late note signing behavior.

Deriving Evidence from Secondary Data in Hand Surgery: Strengths, Limitations, and Future Directions

Health services research using secondary data is a powerful tool for guiding quality/performance measure development, payment reform, and health policy. Patient preferences, physical examination findings, use of postoperative care, and other factors specific to hand surgery research are critical pieces of information required to study quality of care and improve patient outcomes. These data often are missing from data sets, causing limitations and challenges when performing secondary data analyses in hand surgery. As the role of secondary data in surgical research expands, hand surgeons must apply novel strategies and become involved in collaborative initiatives to overcome the limitations of existing resources.

Novel Data Linkages to Characterize Palliative and End-Of-Life Care: Challenges and Considerations

CONTEXT

Working groups have called for linkages of existing and diverse databases to improve quality measurement in palliative and end-of-life (EOL) care, but limited data are available on the challenges of using different data sources to measure such care.

OBJECTIVES

To assess concordance of data obtained from different sources in a novel linkage of death certificates, electronic health records (EHRs), cancer registry data, and insurance claims for patients who died with cancer.

METHODS

We joined a database of Washington State death certificates and EHR to a data repository of commercial health plan enrollment and claims files linked to registry records from Puget Sound Cancer Surveillance System. We assessed care in the last month including hospitalizations, intensive care unit (ICU) admissions, emergency department visits, imaging scans, radiation, and hospice, plus chemotherapy in the last 14 days. We used a Chi-squared test to compare differences between health care in EHR and claims.

RESULTS

Records of hospitalization, ICU use, and emergency department use were 33%, 15%, and 33% lower in EHR versus claims, respectively. Radiation, hospice, and imaging were 6%, 14%, and 28% lower, respectively, in EHR, but chemotherapy was 4% higher than that in claims. These differences were statistically different for hospice (P < 0.02), hospitalization, ICU, ER, and imaging (all P < 0.01) but not radiation (P = 0.12) or chemotherapy (P = 0.29).

CONCLUSION

We found substantial variation between EHR and claims for EOL health-care use. Reliance on EHR will miss some health-care use, while claims will not capture the complex clinical details in EHR that can help define the quality of palliative care and EOL health-care utilization.

"Minimally invasive research?" Use of the electronic health record to facilitate research in pediatric urology

BACKGROUND

The electronic health record (EHR) was designed as a clinical and administrative tool to improve clinical patient care. Electronic healthcare systems have been successfully adopted across the world through use of government mandates and incentives.

METHODS

Using electronic health record, health information system, electronic medical record, health information systems, research, outcomes, pediatric, surgery, and urology as initial search terms, the literature focusing on clinical documentation data capture and the EHR as a potential resource for research related to clinical outcomes, quality improvement, and comparative effectiveness was reviewed. Relevant articles were supplemented by secondary review of article references as well as seminal articles in the field as identified by the senior author.

FINDINGS

US federal funding agencies, including the Agency for Healthcare Research and Quality, the Patient-Centered Outcomes Research Institute, the National Institutes of Health, and the Food and Drug Administration have recognized the EHR's role supporting research. The main approached to using EHR data include enhanced lists, direct data extraction, structured data entry, and unstructured data entry. The EHR's potential to facilitate research, overcoming cost and time burdens associated with traditional data collection, has not resulted in widespread use of EHR-based research tools.

CONCLUSION

There are strengths and weaknesses for all existing methodologies of using EHR data to support research. Collaboration is needed to identify the method that best suits the institution for incorporation of research-oriented data collection into routine pediatric urologic clinical practice.

Primary data, claims data, and linked data in observational research: the case of COPD in Germany

Background

Real-world evidence (RWE) can inform patient management decisions, but RWE studies are associated with limitations. Linkage of different RWE data types could address such limitations by enriching data and improving scientific quality. Using the example of chronic obstructive pulmonary disease (COPD) in Germany, this study assessed the value of data linkage between primary and secondary data sources for RWE.

Methods

Post hoc analysis of data from an observational RWE study, which used prospectively collected data and data from an insurance claims database to assess treatment adherence and persistence in patients with COPD in Germany. Patient-level primary data were collected from the prospective observational study (primary dataset, N = 636), and claims data from the sickness fund AOK Nordost (claims dataset, N = 74,916). Primary and claims data were linked at a patient level using insurance numbers (linked dataset). Patients in the linked dataset were indexed at date of study inclusion for primary data and matched calendar date for claims data. Agreement between primary and claims data was examined for patients in the linked dataset based on comparisons between recorded sociodemographic data at index, comorbidities (primary: any recorded; claims: pre-index), prescriptions for COPD therapies (type and date) and exacerbations in the 12-month post-index period.

Results

The linked dataset included primary and claims data for 536 patients. Fewer comorbid patients were reported in primary data compared with claims data (p < 0.001), with overall agreement between 63.6% (hypertension) and 90.5% (osteoporosis). Number of prescriptions for COPD therapies per patient was lower in primary versus claims data (3.7 vs 10.3 prescriptions, respectively), with only 24.5% of prescriptions recorded in both datasets. Only 11.5% of exacerbations (moderate or severe) were recorded in both datasets, with 15.5% recorded only in primary data and 73.0% recorded only in claims data.

Conclusion

Our study highlighted discrepancies between primary and claims data capture for this population of German patients with COPD, with lower reporting of comorbidities, COPD therapy prescriptions and exacerbations in primary versus claims data. Study findings suggest that data linkage of primary and claims data could provide enrichment and be useful in fully describing COPD endpoints.

Electronic supplementary material

The online version of this article (10.1186/s12931-018-0865-1) contains supplementary material, which is available to authorized users.

An Ontology-Based Knowledge Methodology in the Medical Domain in the Latin America: the Study Case of Republic of Panama

Introduction:

Nowadays in Panama, there is a lot of patient information stored in textual form which cannot be manipulated to manage adequate knowledge. There are multiple resources created to represent knowledge, including specialized glossaries, ontologies, among others. The ontologies are an important part within the scope of the recovery and organization of the information and the semantic web. Also in recent works they are used in applications of natural language processing (NLP), as a knowledge base.

Aim:

This research was conducted with the aim of creating a methodology that allows from a text written in NL, extract the necessary elements using NLP tools and with them create a knowledge base represented by one domain ontology and extract knowledge to help medical specialists.

Material and Methods:

In this study we carried out a methodology that allows the extraction of knowledge of patient clinical records, general medicine and palliative care, in order to show relevant knowledge elements to specialists. The methodology was validated with a data corpus of approximately 200 patient records.

Conclusion:

We have created a knowledge representation methodology, combining NLP techniques and tools and the automatic instantiation of an ontology, which can serve as a software agent for other applications or used to visualize the patient’s clinical information. The study was validated using the traditional metrics of information retrieval systems precision, recall, F-measure obtaining excellent results, and can be used as a software agent or methodology for the development of information extraction software systems in the medical domain.

Evaluating the Impact of Prescription Fill Rates on Risk Stratification Model Performance

BACKGROUND

Risk adjustment models are traditionally derived from administrative claims. Prescription fill rates-extracted by comparing electronic health record prescriptions and pharmacy claims fills-represent a novel measure of medication adherence and may improve the performance of risk adjustment models.

OBJECTIVE

We evaluated the impact of prescription fill rates on claims-based risk adjustment models in predicting both concurrent and prospective costs and utilization.

METHODS

We conducted a retrospective cohort study of 43,097 primary care patients from HealthPartners network between 2011 and 2012. Diagnosis and/or pharmacy claims of 2011 were used to build 3 base models using the Johns Hopkins ACG system, in addition to demographics. Model performances were compared before and after adding 3 types of prescription fill rates: primary 0-7 days, primary 0-30 days, and overall. Overall fill rates utilized all ordered prescriptions from electronic health record while primary fill rates excluded refill orders.

RESULTS

The overall, primary 0-7, and 0-30 days fill rates were 72.30%, 59.82%, and 67.33%. The fill rates were similar between sexes but varied across different medication classifications, whereas the youngest had the highest rate. Adding fill rates modestly improved the performance of all models in explaining medical costs (improving concurrent R by 1.15% to 2.07%), followed by total costs (0.58% to 1.43%), and pharmacy costs (0.07% to 0.65%). The impact was greater for concurrent costs compared with prospective costs. Base models without diagnosis information showed the highest improvement using prescription fill rates.

CONCLUSIONS

Prescription fill rates can modestly enhance claims-based risk prediction models; however, population-level improvements in predicting utilization are limited.

Comparing Population-based Risk-stratification Model Performance Using Demographic, Diagnosis and Medication Data Extracted From Outpatient Electronic Health Records Versus Administrative Claims

BACKGROUND

There is an increasing demand for electronic health record (EHR)-based risk stratification and predictive modeling tools at the population level. This trend is partly due to increased value-based payment policies and the increasing availability of EHRs at the provider level. Risk stratification models, however, have been traditionally derived from claims or encounter systems. This study evaluates the challenges and opportunities of using EHR data instead of or in addition to administrative claims for risk stratification.

METHODS

This study used the structured EHR records and administrative claims of 85,581 patients receiving outpatient care at a large integrated provider system. Common data elements for risk stratification (ie, age, sex, diagnosis, and medication) were extracted from outpatient EHR records and administrative claims. The performance of a validated risk-stratification model was assessed using data extracted from claims alone, EHR alone, and claims and EHR combined.

RESULTS

EHR-derived metrics overlapped considerably with administrative claims (eg, number of chronic conditions). The accuracy of the model, when using EHR data alone, was acceptable with an area under the curve of ∼0.81 for hospitalization and ∼0.85 for identifying top 1% utilizers using the concurrent model. However, when using EHR data alone, the predictive model explained a lower amount of variation in utilization-based outcomes compared with administrative claims.

DISCUSSION

The results show a promising performance of models predicting cost and hospitalization using outpatient EHR's diagnosis and medication data. More research is needed to evaluate the benefits of other EHR data types (eg, lab values and vital signs) for risk stratification.

Extracting autism spectrum disorder data from the electronic health record

BACKGROUND

Little is known about the health care utilization patterns of individuals with pediatric autism spectrum disorder (ASD).

OBJECTIVES

Electronic health record (EHR) data provide an opportunity to study medical utilization and track outcomes among children with ASD.  Methods: Using a pediatric, tertiary, academic hospital's Epic EHR, search queries were built to identify individuals aged 2-18 with International Classification of Diseases, Ninth Revision (ICD-9) codes, 299.00, 299.10, and 299.80 in their records. Codes were entered in the EHR using four different workflows: (1) during an ambulatory visit, (2) abstracted by Health Information Management (HIM) for an encounter, (3) recorded on the patient problem list, or (4) added as a chief complaint during an Emergency Department visit. Once individuals were identified, demographics, scheduling, procedures, and prescribed medications were extracted for all patient-related encounters for the period October 2010 through September 2012.

RESULTS

There were 100,000 encounters for more than 4,800 unique individuals. Individuals were most frequently identified with an HIM abstracted code (82.6%) and least likely to be identified by a chief complaint (45.8%). Categorical frequency for reported race (2 = 816.5, p < 0.001); payor type (2 = 354.1, p < 0.001); encounter type (2 = 1497.0, p < 0.001); and department (2 = 3722.8, p < 0.001) differed by search query. Challenges encountered included, locating available discrete data elements and missing data.

CONCLUSIONS

This study identifies challenges inherent in designing inclusive algorithms for identifying individuals with ASD and demonstrates the utility of employing multiple extractions to improve the completeness and quality of EHR data when conducting research.

Big data for bipolar disorder

The delivery of psychiatric care is changing with a new emphasis on integrated care, preventative measures, population health, and the biological basis of disease. Fundamental to this transformation are big data and advances in the ability to analyze these data. The impact of big data on the routine treatment of bipolar disorder today and in the near future is discussed, with examples that relate to health policy, the discovery of new associations, and the study of rare events. The primary sources of big data today are electronic medical records (EMR), claims, and registry data from providers and payers. In the near future, data created by patients from active monitoring, passive monitoring of Internet and smartphone activities, and from sensors may be integrated with the EMR. Diverse data sources from outside of medicine, such as government financial data, will be linked for research. Over the long term, genetic and imaging data will be integrated with the EMR, and there will be more emphasis on predictive models. Many technical challenges remain when analyzing big data that relates to size, heterogeneity, complexity, and unstructured text data in the EMR. Human judgement and subject matter expertise are critical parts of big data analysis, and the active participation of psychiatrists is needed throughout the analytical process.

Access Scheme for Controlling Mobile Agents and its Application to Share Medical Information

This study is showing the advantage of mobile agents to conquer heterogeneous system environments and contribute to a virtual integrated sharing system. Mobile agents will collect medical information from each medical institution as a method to achieve the medical purpose of data sharing. Besides, this research also provides an access control and key management mechanism by adopting Public key cryptography and Lagrange interpolation. The safety analysis of the system is based on a network attacker's perspective. The achievement of this study tries to improve the medical quality, prevent wasting medical resources and make medical resources access to appropriate configuration.

Bidirectional Data Collaborations in Distributed Research

Introduction:

The need for collaborations with bidirectional data exchange within and across distributed research networks has increased.

Currently Existing Activities:

This commentary will present currently publically available activities including the Sentinel Initiative, the Patient-Centered Outcomes Research Network (PCORnet), and the NIH Research Collaboratory.

Current Technical and Governance Challenges:

Even with the advances made in this arena, several technical and governance challenges remain including the evolution of clinically rich data sources and modes of care, availability of longitudinal data resources through data linkage, and the processes to share data and link data resources while ensuring privacy and proprietary control of data.

Perspective:

These activities will require enhanced levels of trust between entities involved in the delivery of healthcare (Trust 2.0) in addition to the trust health plans and health systems have with patients (Trust 1.0). Recent public funding announcements and public access to data resources will likely improve the landscape of bidirectional data collaborations in distributed research.