Implementing Single Source: the STARBRITE proof-of-concept study

Data Flow Construction and Quality Evaluation of Electronic Source Data in Clinical Trials: Pilot Study Based on Hospital Electronic Medical Records in China

Background

The traditional clinical trial data collection process requires a clinical research coordinator who is authorized by the investigators to read from the hospital's electronic medical record. Using electronic source data opens a new path to extract patients' data from electronic health records (EHRs) and transfer them directly to an electronic data capture (EDC) system; this method is often referred to as eSource. eSource technology in a clinical trial data flow can improve data quality without compromising timeliness. At the same time, improved data collection efficiency reduces clinical trial costs.

Objective

This study aims to explore how to extract clinical trial-related data from hospital EHR systems, transform the data into a format required by the EDC system, and transfer it into sponsors' environments, and to evaluate the transferred data sets to validate the availability, completeness, and accuracy of building an eSource dataflow.

Methods

A prospective clinical trial study registered on the Drug Clinical Trial Registration and Information Disclosure Platform was selected, and the following data modules were extracted from the structured data of 4 case report forms: demographics, vital signs, local laboratory data, and concomitant medications. The extracted data was mapped and transformed, deidentified, and transferred to the sponsor's environment. Data validation was performed based on availability, completeness, and accuracy.

Results

In a secure and controlled data environment, clinical trial data was successfully transferred from a hospital EHR to the sponsor's environment with 100% transcriptional accuracy, but the availability and completeness of the data could be improved.

Conclusions

Data availability was low due to some required fields in the EDC system not being available directly in the EHR. Some data is also still in an unstructured or paper-based format. The top-level design of the eSource technology and the construction of hospital electronic data standards should help lay a foundation for a full electronic data flow from EHRs to EDC systems in the future.

Integrating REDCap patient-reported outcomes with the HealtheIntent population health platform: proof of concept

Objective

Patient-reported outcome measures (PROMs) are critical to drive patient-centered care and to understanding patients' perspectives on their health status, quality of life, and the overall effectiveness of the care they receive. PROMs are increasingly being used in clinical and research settings, but the mechanisms to aggregate data from different systems can be cumbersome.

Materials and methods

As part of an FDA Real-World Evidence demonstration project, we enriched routine care clinical data from our Cerner electronic health record (EHR) with PROMs collected using REDCap. We used SSIS, sFTP, and the REDCap Application Programming Interface to aggregate both data sources into the Cerner HealtheIntent Population Health Platform.

Results

We successfully built dashboards, reports, and datasets containing both REDCap and EHR data collected prospectively.

Discussion

This technically straightforward approach using commonly available clinical and research tools can be readily adopted and adapted by others to better integrate PROMs with clinical data sources.

Challenges and Solutions in Implementing eSource Technology for Real-World Studies in China: Qualitative Study Among Different Stakeholders

BACKGROUND

eSources consist of data that were initially documented in an electronic structure. Typically, an eSource encompasses the direct acquisition, compilation, and retention of electronic information (such as electronic health records [EHRs] or wearable devices), which serves to streamline clinical research. eSources have the potential to enhance the accuracy of data, promote patient safety, and minimize expenses associated with clinical trials. An opinion study published in September 2020 by TransCelerate outlined a road map for the future application of eSource technology and identified 5 key areas of challenges. The background of this study concerns the use of eSource technology in clinical research.

OBJECTIVE

The aim of this study was to present challenges and possible solutions for the implementation of eSource technology in real-world studies by summarizing team experiences and lessons learned from an eSource record (ESR) project.

METHODS

After initially developing a simple prototype of the ESR software that can be demonstrated systematically, the researchers conducted in-depth interviews and interacted with different stakeholders to obtain guidance and suggestions. The researchers selected 5 different roles for interviewees: regulatory authorities, pharmaceutical company representatives, hospital information department employees, medical system providers, and clinicians.

RESULTS

After screening all consultants, the researchers concluded that there were 25 representative consultants. The hospital information department needs to implement many demands from various stakeholders, which makes the existing EHR system unable to meet all the demands of eSources. The emergence of an ESR is intended to divert the burden of the hospital information department from the enormous functional requirements of the outdated EHR system. The entire research process emphasizes multidisciplinary and multibackground expert opinions and considers the complexity of the knowledge backgrounds of personnel involved in the chain of clinical source data collection, processing, quality control, and application in real-world scenarios. To increase the readability of the results, the researchers classified the main results in accordance with the paragraph titles in "Use of Electronic Health Record Data in Clinical Investigations," a guide released by the US Food and Drug Administration.

CONCLUSIONS

This study introduces the requirement dependencies of different stakeholders and the challenges and recommendations for designing ESR software when implementing eSource technology in China. Experiences based on ESR projects will provide new insights into the disciplines that advance the eSource research field. Future studies should engage patients directly to understand their experiences, concerns, and preferences regarding the implementation of eSource technology. Moreover, involving additional stakeholders, including community health care providers and social workers, will provide valuable insights into the challenges and potential solutions across various health care settings.

Clinical Source Data Production and Quality Control in Real-world Studies: Proposal for Development of the eSource Record System

BACKGROUND

An eSource generally includes the direct capture, collection, and storage of electronic data to simplify clinical research. It can improve data quality and patient safety and reduce clinical trial costs. There has been some eSource-related research progress in relatively large projects. However, most of these studies focused on technical explorations to improve interoperability among systems to reuse retrospective data for research. Few studies have explored source data collection and quality control during prospective data collection from a methodological perspective.

OBJECTIVE

This study aimed to design a clinical source data collection method that is suitable for real-world studies and meets the data quality standards for clinical research and to improve efficiency when writing electronic medical records (EMRs).

METHODS

On the basis of our group's previous research experience, TransCelerate BioPharm Inc eSource logical architecture, and relevant regulations and guidelines, we designed a source data collection method and invited relevant stakeholders to optimize it. On the basis of this method, we proposed the eSource record (ESR) system as a solution and invited experts with different roles in the contract research organization company to discuss and design a flowchart for data connection between the ESR and electronic data capture (EDC).

RESULTS

The ESR method included 5 steps: research project preparation, initial survey collection, in-hospital medical record writing, out-of-hospital follow-up, and electronic case report form (eCRF) traceability. The data connection between the ESR and EDC covered the clinical research process from creating the eCRF to collecting data for the analysis. The intelligent data acquisition function of the ESR will automatically complete the empty eCRF to create an eCRF with values. When the clinical research associate and data manager conduct data verification, they can query the certified copy database through interface traceability and send data queries. The data queries are transmitted to the ESR through the EDC interface. The EDC and EMR systems interoperate through the ESR. The EMR and EDC systems transmit data to the ESR system through the data standards of the Health Level Seven Clinical Document Architecture and the Clinical Data Interchange Standards Consortium operational data model, respectively. When the implemented data standards for a given system are not consistent, the ESR will approach the problem by first automating mappings between standards and then handling extensions or corrections to a given data format through human evaluation.

CONCLUSIONS

The source data collection method proposed in this study will help to realize eSource's new strategy. The ESR solution is standardized and sustainable. It aims to ensure that research data meet the attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, and available standards for clinical research data quality and to provide a new model for prospective data collection in real-world studies.

Immediate oral versus immediate topical versus delayed oral antibiotics for children with acute otitis media with discharge: the REST three-arm non-inferiority electronic platform-supported RCT

BACKGROUND

Acute otitis media is a painful infection of the middle ear that is commonly seen in children. In some children, the eardrum spontaneously bursts, discharging visible pus (otorrhoea) into the outer ear.

OBJECTIVE

To compare the clinical effectiveness of immediate topical antibiotics or delayed oral antibiotics with the clinical effectiveness of immediate oral antibiotics in reducing symptom duration in children presenting to primary care with acute otitis media with discharge and the economic impact of the alternative strategies.

DESIGN

This was a pragmatic, three-arm, individually randomised (stratified by age < 2 vs. ≥ 2 years), non-inferiority, open-label trial, with economic and qualitative evaluations, supported by a health-record-integrated electronic trial platform [TRANSFoRm (Translational Research and Patient Safety in Europe)] with an internal pilot.

SETTING

A total of 44 English general practices.

PARTICIPANTS

Children aged ≥ 12 months and < 16 years whose parents (or carers) were seeking medical care for unilateral otorrhoea (ear discharge) following recent-onset (≤ 7 days) acute otitis media.

INTERVENTIONS

(1) Immediate ciprofloxacin (0.3%) solution, four drops given three times daily for 7 days, or (2) delayed 'dose-by-age' amoxicillin suspension given three times daily (clarithromycin twice daily if the child was penicillin allergic) for 7 days, with structured delaying advice. All parents were given standardised information regarding symptom management (paracetamol/ibuprofen/fluids) and advice to complete the course.

COMPARATOR

Immediate 'dose-by-age' oral amoxicillin given three times daily (or clarithromycin given twice daily) for 7 days. Parents received standardised symptom management advice along with advice to complete the course.

MAIN OUTCOME MEASURE

Time from randomisation to the first day on which all symptoms (pain, fever, being unwell, sleep disturbance, otorrhoea and episodes of distress/crying) were rated 'no' or 'very slight' problem (without need for analgesia).

METHODS

Participants were recruited from routine primary care appointments. The planned sample size was 399 children. Follow-up used parent-completed validated symptom diaries.

RESULTS

Delays in software deployment and configuration led to small recruitment numbers and trial closure at the end of the internal pilot. Twenty-two children (median age 5 years; 62% boys) were randomised: five, seven and 10 to immediate oral, delayed oral and immediate topical antibiotics, respectively. All children received prescriptions as randomised. Seven (32%) children fully adhered to the treatment as allocated. Symptom duration data were available for 17 (77%) children. The median (interquartile range) number of days until symptom resolution in the immediate oral, delayed oral and immediate topical antibiotic arms was 6 (4-9), 4 (3-7) and 4 (3-6), respectively. Comparative analyses were not conducted because of small numbers. There were no serious adverse events and six reports of new or worsening symptoms. Qualitative clinician interviews showed that the trial question was important. When the platform functioned as intended, it was liked. However, staff reported malfunctioning software for long periods, resulting in missed recruitment opportunities. Troubleshooting the software placed significant burdens on staff.

LIMITATIONS

The over-riding weakness was the failure to recruit enough children.

CONCLUSIONS

We were unable to answer the main research question because of a failure to reach the required sample size. Our experience of running an electronic platform-supported trial in primary care has highlighted challenges from which we have drawn recommendations for the National Institute for Health Research (NIHR) and the research community. These should be considered before such a platform is used again.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN12873692 and EudraCT 2017-003635-10.

FUNDING

This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 67. See the NIHR Journals Library website for further project information.

Clinical data for paediatric research: the Swiss approach : Proceedings of the National Symposium in Bern, Switzerland, Dec 5-6, 2019

BACKGROUND AND PURPOSE

Continuous improvement of health and healthcare system is hampered by inefficient processes of generating new evidence, particularly in the case of rare diseases and paediatrics. Currently, most evidence is generated through specific research projects, which typically require extra encounters with patients, are costly and entail long delays between the recognition of specific needs in healthcare and the generation of necessary evidence to address those needs. The Swiss Personalised Health Network (SPHN) aims to improve the use of data obtained during routine healthcare encounters by harmonizing data across Switzerland and facilitating accessibility for research. The project "Harmonising the collection of health-related data and biospecimens in paediatric hospitals throughout Switzerland (SwissPedData)" was an infrastructure development project funded by the SPHN, which aimed to identify and describe available data on child health in Switzerland and to agree on a standardised core dataset for electronic health records across all paediatric teaching hospitals. Here, we describe the results of a two-day symposium that aimed to summarise what had been achieved in the SwissPedData project, to put it in an international context, and to discuss the next steps for a sustainable future. The target audience included clinicians and researchers who produce and use health-related data on children in Switzerland.

KEY HIGHLIGHTS

The symposium consisted of state-of-the-art lectures from national and international keynote speakers, workshops and plenary discussions. This manuscript summarises the talks and discussions in four sections: (I) a description of the Swiss Personalized Health Network and the results of the SwissPedData project; (II) examples of similar initiatives from other countries; (III) an overview of existing health-related datasets and projects in Switzerland; and (IV) a summary of the lessons learned and future prospective from workshops and plenary discussions.

IMPLICATIONS

Streamlined processes linking initial collection of information during routine healthcare encounters, standardised recording of this information in electronic health records and fast accessibility for research are essential to accelerate research in child health and make it affordable. Ongoing projects prove that this is feasible in Switzerland and elsewhere. International collaboration is vital to success. The next steps include the implementation of the SwissPedData core dataset in the clinical information systems of Swiss hospitals, the use of this data to address priority research questions, and the acquisition of sustainable funding to support a slim central infrastructure and local support in each hospital. This will lay the foundation for a national paediatric learning health system in Switzerland.

Evaluating the Coverage of the HL7 ® FHIR ® Standard to Support eSource Data Exchange Implementations for use in Multi-Site Clinical Research Studies

The direct use of EHR data in research, often referred to as 'eSource', has long-been a goal for researchers because of anticipated increases in data quality and reductions in site burden. eSource solutions should rely on data exchange standards for consistency, quality, and efficiency. The utility of any data standard can be evaluated by its ability to meet specific use case requirements. The Health Level Seven (HL7 ^® ) Fast Healthcare Interoperability Resources (FHIR ^® ) standard is widely recognized for clinical data exchange; however, a thorough analysis of the standard's data coverage in supporting multi-site clinical studies has not been conducted. We developed and implemented a systematic mapping approach for evaluating HL7 ^® FHIR ^® standard coverage in multi-center clinical trials. Study data elements from three diverse studies were mapped to HL7 ^® FHIR ^® resources, offering insight into the coverage and utility of the standard for supporting the data collection needs of multi-site clinical research studies.

Comparison and transformation between CDISC ODM and EN13606 EHR standards in connecting EHR data with clinical trial research data

Objectives

Integrating Electronic Health Record (EHR) systems into the field of clinical trials still contains several challenges and obstacles. Heterogeneous standards and specifications are used to represent healthcare and clinical trial information. Therefore, this work investigates the mapping and data interoperability between healthcare and research standards: EN13606 used for the EHRs and the Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) used for clinical research.

Methods

Based on the specifications of CDISC ODM 1.3.2 and EN13606, a mapping between the structure and components of both standards has been performed. Archetype Definition Language (ADL) forms built with the EN13606 editor were transformed to ODM XML and reviewed. As a proof of concept, clinical sample data has been transformed into ODM and imported into an electronic data capture system. Reverse transformation from ODM to ADL has also been performed and finally reviewed concerning map-ability.

Results

The mapping between EN13606 and CDISC ODM shows the similarities and differences between the components and overall record structure of the two standards. An EN13606 archetype corresponds with a group of items within CDISC ODM. Transformations of element names, descriptions, different languages, datatypes, cardinality, optionality, units, value range and terminology codes are possible from EN13606 to CDISC ODM and vice versa.

Conclusion

It is feasible to map data elements between EN13606 and CDISC ODM and transformation of forms between ADL and ODM XML format is possible with only minor limitations. EN13606 can accommodate clinical information in a more structured manner with more constraints, whereas CDISC ODM is more suitable and specific for clinical trials and studies. It is feasible to transform EHR data in the EN13606 form to ODM to transfer it into research database. The attempt to use EN13606 to build a study protocol (that was already built with CDISC ODM) also suggests the possibility of using EN13606 standard in place of CDISC ODM if needed to avoid transformations.

Generalizable Middleware to Support Use of REDCap Dynamic Data Pull for Integrating Clinical and Research Data

To support integration of clinical and research data, the makers of REDCap, a widely-used electronic data capture system, released the Dynamic Data Pull (DDP) module. Although DDP is a standard module in REDCap, institutions must develop custom middleware web services to exchange data between REDCap and local source systems. The lack of middleware is a barrier to institutional adoption and use by investigators. To overcome this gap, we developed a REDCap DDP web service middleware (accessible at https://github.com/wcmc-research-informatics/redcap-ddp) that minimizes developer effort, relies on configuration by non-developers, and can generalize to other settings. Early findings suggest the approach is successful.

Integrating Medication Alert Data into a Clinical Data Repository to Enable Retrospective Study of Drug Interaction Alerts in Clinical Practice

Current clinical data repositories primarily extract data from multiple administrative and electronic medical record (EMR) data resources (e.g., hospital and physician billing records) containing specific patient-level data including demographics, medications, laboratory results, diagnoses, and procedure codes. It overlooks the importance of EMR system-level data (e.g., medication alerts that are routinely used by physicians, nurses, and pharmacists for decision support) for the surveillance of EMR decision support tools. These medication alerts are a significant source of information for providers, to minimize avoidable adverse drug events. This study describes the integration of medication alert data into an i2b2-based clinical data repository to support the investigation of clinical events occurring around patients with anticoagulation treatment that triggered drug-drug interaction alerts. The integration of medication alerts allows us to repurpose the clinical and translational research infrastructure to conduct retrospective effectiveness surveillance of clinical decision support tools.

eSource for clinical trials: Implementation and evaluation of a standards-based approach in a real world trial

OBJECTIVE

The Learning Health System (LHS) requires integration of research into routine practice. 'eSource' or embedding clinical trial functionalities into routine electronic health record (EHR) systems has long been put forward as a solution to the rising costs of research. We aimed to create and validate an eSource solution that would be readily extensible as part of a LHS.

MATERIALS AND METHODS

The EU FP7 TRANSFoRm project's approach is based on dual modelling, using the Clinical Research Information Model (CRIM) and the Clinical Data Integration Model of meaning (CDIM) to bridge the gap between clinical and research data structures, using the CDISC Operational Data Model (ODM) standard. Validation against GCP requirements was conducted in a clinical site, and a cluster randomised evaluation by site nested into a live clinical trial.

RESULTS

Using the form definition element of ODM, we linked precisely modelled data queries to data elements, constrained against CDIM concepts, to enable automated patient identification for specific protocols and pre-population of electronic case report forms (e-CRF). Both control and eSource sites recruited better than expected with no significant difference. Completeness of clinical forms was significantly improved by eSource, but Patient Related Outcome Measures (PROMs) were less well completed on smartphones than paper in this population.

DISCUSSION

The TRANSFoRm approach provides an ontologically-based approach to eSource in a low-resource, heterogeneous, highly distributed environment, that allows precise prospective mapping of data elements in the EHR.

CONCLUSION

Further studies using this approach to CDISC should optimise the delivery of PROMS, whilst building a sustainable infrastructure for eSource with research networks, trials units and EHR vendors.

A SOA-Based Platform to Support Clinical Data Sharing

The eSource Data Interchange Group, part of the Clinical Data Interchange Standards Consortium, proposed five scenarios to guide stakeholders in the development of solutions for the capture of eSource data. The fifth scenario was subdivided into four tiers to adapt the functionality of electronic health records to support clinical research. In order to develop a system belonging to the "Interoperable" Tier, the authors decided to adopt the service-oriented architecture paradigm to support technical interoperability, Health Level Seven Version 3 messages combined with LOINC (Logical Observation Identifiers Names and Codes) vocabulary to ensure semantic interoperability, and Healthcare Services Specification Project standards to provide process interoperability. The developed architecture enhances the integration between patient-care practice and medical research, allowing clinical data sharing between two hospital information systems and four clinical data management systems/clinical registries. The core is formed by a set of standardized cloud services connected through standardized interfaces, involving client applications. The system was approved by a medical staff, since it reduces the workload for the management of clinical trials. Although this architecture can realize the "Interoperable" Tier, the current solution actually covers the "Connected" Tier, due to local hospital policy restrictions.

Cross border semantic interoperability for clinical research: the EHR4CR semantic resources and services

With the development of platforms enabling the use of routinely collected clinical data in the context of international clinical research, scalable solutions for cross border semantic interoperability need to be developed. Within the context of the IMI EHR4CR project, we first defined the requirements and evaluation criteria of the EHR4CR semantic interoperability platform and then developed the semantic resources and supportive services and tooling to assist hospital sites in standardizing their data for allowing the execution of the project use cases. The experience gained from the evaluation of the EHR4CR platform accessing to semantically equivalent data elements across 11 European participating EHR systems from 5 countries demonstrated how far the mediation model and mapping efforts met the expected requirements of the project. Developers of semantic interoperability platforms are beginning to address a core set of requirements in order to reach the goal of developing cross border semantic integration of data.

Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

INTRODUCTION

In order to further advance research and development on the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) standard, the existing research must be well understood. This paper presents a methodological review of the ODM literature. Specifically, it develops a classification schema to categorize the ODM literature according to how the standard has been applied within the clinical research data lifecycle. This paper suggests areas for future research and development that address ODM's limitations and capitalize on its strengths to support new trends in clinical research informatics.

METHODS

A systematic scan of the following databases was performed: (1) ABI/Inform, (2) ACM Digital, (3) AIS eLibrary, (4) Europe Central PubMed, (5) Google Scholar, (5) IEEE Xplore, (7) PubMed, and (8) ScienceDirect. A Web of Science citation analysis was also performed. The search term used on all databases was "CDISC ODM." The two primary inclusion criteria were: (1) the research must examine the use of ODM as an information system solution component, or (2) the research must critically evaluate ODM against a stated solution usage scenario. Out of 2686 articles identified, 266 were included in a title level review, resulting in 183 articles. An abstract review followed, resulting in 121 remaining articles; and after a full text scan 69 articles met the inclusion criteria.

RESULTS

As the demand for interoperability has increased, ODM has shown remarkable flexibility and has been extended to cover a broad range of data and metadata requirements that reach well beyond ODM's original use cases. This flexibility has yielded research literature that covers a diverse array of topic areas. A classification schema reflecting the use of ODM within the clinical research data lifecycle was created to provide a categorized and consolidated view of the ODM literature. The elements of the framework include: (1) EDC (Electronic Data Capture) and EHR (Electronic Health Record) infrastructure; (2) planning; (3) data collection; (4) data tabulations and analysis; and (5) study archival. The analysis reviews the strengths and limitations of ODM as a solution component within each section of the classification schema. This paper also identifies opportunities for future ODM research and development, including improved mechanisms for semantic alignment with external terminologies, better representation of the CDISC standards used end-to-end across the clinical research data lifecycle, improved support for real-time data exchange, the use of EHRs for research, and the inclusion of a complete study design.

CONCLUSIONS

ODM is being used in ways not originally anticipated, and covers a diverse array of use cases across the clinical research data lifecycle. ODM has been used as much as a study metadata standard as it has for data exchange. A significant portion of the literature addresses integrating EHR and clinical research data. The simplicity and readability of ODM has likely contributed to its success and broad implementation as a data and metadata standard. Keeping the core ODM model focused on the most fundamental use cases, while using extensions to handle edge cases, has kept the standard easy for developers to learn and use.

An Interoperability Platform Enabling Reuse of Electronic Health Records for Signal Verification Studies

Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations and tracing the reported incidents back to original EHRs. We have developed an ontological framework where EHR sources and target clinical research systems can continue using their own local data models, interfaces, and terminology systems, while structural interoperability and Semantic Interoperability are handled through rule-based reasoning on formal representations of different models and terminology systems maintained in the SALUS Semantic Resource Set. SALUS Common Information Model at the core of this set acts as the common mediator. We demonstrate the capabilities of our framework through one of the SALUS safety analysis tools, namely, the Case Series Characterization Tool, which have been deployed on top of regional EHR Data Warehouse of the Lombardy Region containing about 1 billion records from 16 million patients and validated by several pharmacovigilance researchers with real-life cases. The results confirm significant improvements in signal detection and evaluation compared to traditional methods with the missing background information.

Health Information Technology: Meaningful Use and Next Steps to Improving Electronic Facilitation of Medication Adherence

BACKGROUND

The use of health information technology (HIT) may improve medication adherence, but challenges for implementation remain.

OBJECTIVE

The aim of this paper is to review the current state of HIT as it relates to medication adherence programs, acknowledge the potential barriers in light of current legislation, and provide recommendations to improve ongoing medication adherence strategies through the use of HIT.

METHODS

We describe four potential HIT barriers that may impact interoperability and subsequent medication adherence. Legislation in the United States has incentivized the use of HIT to facilitate and enhance medication adherence. The Health Information Technology for Economic and Clinical Health (HITECH) was recently adopted and establishes federal standards for the so-called "meaningful use" of certified electronic health record (EHR) technology that can directly impact medication adherence.

RESULTS

The four persistent HIT barriers to medication adherence include (1) underdevelopment of data reciprocity across clinical, community, and home settings, limiting the capture of data necessary for clinical care; (2) inconsistent data definitions and lack of harmonization of patient-focused data standards, making existing data difficult to use for patient-centered outcomes research; (3) inability to effectively use the national drug code information from the various electronic health record and claims datasets for adherence purposes; and (4) lack of data capture for medication management interventions, such as medication management therapy (MTM) in the EHR. Potential recommendations to address these issues are discussed.

CONCLUSION

To make meaningful, high quality data accessible, and subsequently improve medication adherence, these challenges will need to be addressed to fully reach the potential of HIT in impacting one of our largest public health issues.

Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM)

Efficient communication of a clinical study protocol and case report forms during all stages of a human clinical study is important for many stakeholders. An electronic and structured study representation format that can be used throughout the whole study life-span can improve such communication and potentially lower total study costs. The most relevant standard for representing clinical study data, applicable to unregulated as well as regulated studies, is the Operational Data Model (ODM) in development since 1999 by the Clinical Data Interchange Standards Consortium (CDISC). ODM's initial objective was exchange of case report forms data but it is increasingly utilized in other contexts. An ODM extension called Study Design Model, introduced in 2011, provides additional protocol representation elements. Using a case study approach, we evaluated ODM's ability to capture all necessary protocol elements during a complete clinical study lifecycle in the Intramural Research Program of the National Institutes of Health. ODM offers the advantage of a single format for institutions that deal with hundreds or thousands of concurrent clinical studies and maintain a data warehouse for these studies. For each study stage, we present a list of gaps in the ODM standard and identify necessary vendor or institutional extensions that can compensate for such gaps. The current version of ODM (1.3.2) has only partial support for study protocol and study registration data mainly because it is outside the original development goal. ODM provides comprehensive support for representation of case report forms (in both the design stage and with patient level data). Inclusion of requirements of observational, non-regulated or investigator-initiated studies (outside Food and Drug Administration (FDA) regulation) can further improve future revisions of the standard.

Ontology-based data integration between clinical and research systems

Data from the electronic medical record comprise numerous structured but uncoded ele-ments, which are not linked to standard terminologies. Reuse of such data for secondary research purposes has gained in importance recently. However, the identification of rele-vant data elements and the creation of database jobs for extraction, transformation and loading (ETL) are challenging: With current methods such as data warehousing, it is not feasible to efficiently maintain and reuse semantically complex data extraction and trans-formation routines. We present an ontology-supported approach to overcome this challenge by making use of abstraction: Instead of defining ETL procedures at the database level, we use ontologies to organize and describe the medical concepts of both the source system and the target system. Instead of using unique, specifically developed SQL statements or ETL jobs, we define declarative transformation rules within ontologies and illustrate how these constructs can then be used to automatically generate SQL code to perform the desired ETL procedures. This demonstrates how a suitable level of abstraction may not only aid the interpretation of clinical data, but can also foster the reutilization of methods for un-locking it.

p-medicine: A Medical Informatics Platform for Integrated Large Scale Heterogeneous Patient Data

Secure access to patient data is becoming of increasing importance, as medical informatics grows in significance, to both assist with population health studies, and patient specific medicine in support of treatment. However, assembling the many different types of data emanating from the clinic is in itself a difficulty, and doing so across national borders compounds the problem. In this paper we present our solution: an easy to use distributed informatics platform embedding a state of the art data warehouse incorporating a secure pseudonymisation system protecting access to personal healthcare data. Using this system, a whole range of patient derived data, from genomics to imaging to clinical records, can be assembled and linked, and then connected with analytics tools that help us to understand the data. Research performed in this environment will have immediate clinical impact for personalised patient healthcare.

Does single-source create an added value? Evaluating the impact of introducing x4T into the clinical routine on workflow modifications, data quality and cost-benefit

OBJECTIVES

The first objective of this study is to evaluate the impact of integrating a single-source system into the routine patient care documentation workflow with respect to process modifications, data quality and execution times in patient care as well as research documentation. The second one is to evaluate whether it is cost-efficient using a single-source system in terms of achieved savings in documentation expenditures.

METHODS

We analyzed the documentation workflow of routine patient care and research documentation in the medical field of pruritus to identify redundant and error-prone process steps. Based on this, we established a novel documentation workflow including the x4T (exchange for Trials) system to connect hospital information systems with electronic data capture systems for the exchange of study data. To evaluate the workflow modifications, we performed a before/after analysis as well as a time-motion study. Data quality was assessed by measuring completeness, correctness and concordance of previously and newly collected data. A cost-benefit analysis was conducted to estimate the savings using x4T per collected data element and the additional costs for introducing x4T.

RESULTS

The documentation workflow of patient care as well as clinical research was modified due to the introduction of the x4T system. After x4T implementation and workflow modifications, half of the redundant and error-prone process steps were eliminated. The generic x4T system allows direct transfer of routinely collected health care data into the x4T research database and avoids manual transcription steps. Since x4T has been introduced in March 2012, the number of included patients has increased by about 1000 per year. The average entire documentation time per patient visit has been significantly decreased by 70.1% (from 1116±185 to 334±83 s). After the introduction of the x4T system and associated workflow changes, the completeness of mandatory data elements raised from 82.2% to 100%. In case of the pruritus research study, the additional costs for introducing the x4T system are €434.01 and the savings are 0.48ct per collected data element. So, with the assumption of a 5-year runtime and 82 collected data elements per patient, the amount of documented patients has to be higher than 1102 to create a benefit.

CONCLUSION

Introduction of the x4T system into the clinical and research documentation workflow can optimize the data collection workflow in both areas. Redundant and cumbersome process steps can be eliminated in the research documentation, with the result of reduced documentation times as well as increased data quality. The usage of the x4T system is especially worthwhile in a study with a large amount of collected data or a high number of included patients.

Standard-based EHR-enabled applications for clinical research and patient safety: CDISC - IHE QRPH - EHR4CR & SALUS collaboration

Integration profiles collaboratively developed by CDISC and IHE for integrating data from Electronic Health Records (EHRs) with clinical research and pharmacovigilance are limited to resolving lexical/syntactic data integration issues and do not address semantic barriers. This paper describes the collaboration between two European projects - EHR4CR and SALUS - in implementing ISO/IEC 11179-based metadata registries (MDRs) and semantically integrated cross-platform data access. A common "semantic MDR" provides a framework for bidirectional/cross-MDR mapping and federated queries are enabled using the newly-defined IHE Data Exchange (DEX) profile. In the pilot implementation, mappings for 178 EHR4CR and 199 SALUS metadata elements were persisted in the semantic MDR. The DEX profile was then used to access semantically equivalent data elements in SALUS or EHR4CR participating EHR systems. ISO/IEC 11179-based MDRs and DEX integration profile address the goal of developing pan-EU computable semantic integration of data from clinical care, clinical research, and patient safety platforms.

Comparison of two data collection processes in clinical studies: electronic and paper case report forms

Background

Electronic Case Report Forms (eCRFs) are increasingly chosen by investigators and sponsors of clinical research instead of the traditional pen-and-paper data collection (pCRFs). Previous studies suggested that eCRFs avoided mistakes, shortened the duration of clinical studies and reduced data collection costs.

Methods

Our objectives were to describe and contrast both objective and subjective efficiency of pCRF and eCRF use in clinical studies. A total of 27 studies (11 eCRF, 16 pCRF) sponsored by the Paris hospital consortium, conducted and completed between 2001 and 2011 were included. Questionnaires were emailed to investigators of those studies, as well as clinical research associates and data managers working in Paris hospitals, soliciting their level of satisfaction and preferences for eCRFs and pCRFs. Mean costs and timeframes were compared using bootstrap methods, linear and logistic regression.

Results

The total cost per patient was 374€ ±351 with eCRFs vs. 1,135€ ±1,234 with pCRFs. Time between the opening of the first center and the database lock was 31.7 months Q1 = 24.6; Q3 = 42.8 using eCRFs, vs. 39.8 months Q1 = 31.7; Q3 = 52.2 with pCRFs (p = 0.11). Electronic CRFs were globally preferred by all (31/72 vs. 15/72 for paper) for easier monitoring and improved data quality.

Conclusions

This study found that eCRFs and pCRFs are used in studies with different patient numbers, center numbers and risk. The first ones are more advantageous in large, low–risk studies and gain support from a majority of stakeholders.

The National Institutes of Health's Biomedical Translational Research Information System (BTRIS): design, contents, functionality and experience to date

The US National Institutes of Health (NIH) has developed the Biomedical Translational Research Information System (BTRIS) to support researchers' access to translational and clinical data. BTRIS includes a data repository, a set of programs for loading data from NIH electronic health records and research data management systems, an ontology for coding the disparate data with a single terminology, and a set of user interface tools that provide access to identified data from individual research studies and data across all studies from which individually identifiable data have been removed. This paper reports on unique design elements of the system, progress to date and user experience after five years of development and operation.

Missing signposts on the roadmap to quality: a call to improve medication adherence indicators in data collection for population research

PURPOSE

Poor adherence to prescribed medicines is associated with increased rates of poor outcomes, including hospitalization, serious adverse events, and death, and is also associated with increased healthcare costs. However, current approaches to evaluation of medication adherence using real-world electronic health records (EHRs) or claims data may miss critical opportunities for data capture and fall short in modeling and representing the full complexity of the healthcare environment. We sought to explore a framework for understanding and improving data capture for medication adherence in a population-based intervention in four U.S. counties.

APPROACH

We posited that application of a data model and a process matrix when designing data collection for medication adherence would improve identification of variables and data accessibility, and could support future research on medication-taking behaviors. We then constructed a use case in which data related to medication adherence would be leveraged to support improved healthcare quality, clinical outcomes, and efficiency of healthcare delivery in a population-based intervention for persons with diabetes. Because EHRs in use at participating sites were deemed incapable of supplying the needed data, we applied a taxonomic approach to identify and define variables of interest. We then applied a process matrix methodology, in which we identified key research goals and chose optimal data domains and their respective data elements, to instantiate the resulting data model.

CONCLUSIONS

Combining a taxonomic approach with a process matrix methodology may afford significant benefits when designing data collection for clinical and population-based research in the arena of medication adherence. Such an approach can effectively depict complex real-world concepts and domains by "mapping" the relationships between disparate contributors to medication adherence and describing their relative contributions to the shared goals of improved healthcare quality, outcomes, and cost.

Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial

INTRODUCTION

Collecting trial data in a medical environment is at present mostly performed manually and therefore time-consuming, prone to errors and often incomplete with the complex data considered. Faster and more accurate methods are needed to improve the data quality and to shorten data collection times where information is often scattered over multiple data sources. The purpose of this study is to investigate the possible benefit of modern data warehouse technology in the radiation oncology field.

MATERIAL AND METHODS

In this study, a Computer Aided Theragnostics (CAT) data warehouse combined with automated tools for feature extraction was benchmarked against the regular manual data-collection processes. Two sets of clinical parameters were compiled for non-small cell lung cancer (NSCLC) and rectal cancer, using 27 patients per disease. Data collection times and inconsistencies were compared between the manual and the automated extraction method.

RESULTS

The average time per case to collect the NSCLC data manually was 10.4 ± 2.1 min and 4.3 ± 1.1 min when using the automated method (p<0.001). For rectal cancer, these times were 13.5 ± 4.1 and 6.8 ± 2.4 min, respectively (p<0.001). In 3.2% of the data collected for NSCLC and 5.3% for rectal cancer, there was a discrepancy between the manual and automated method.

CONCLUSIONS

Aggregating multiple data sources in a data warehouse combined with tools for extraction of relevant parameters is beneficial for data collection times and offers the ability to improve data quality. The initial investments in digitizing the data are expected to be compensated due to the flexibility of the data analysis. Furthermore, successive investigations can easily select trial candidates and extract new parameters from the existing databases.

Secondary use of routinely collected patient data in a clinical trial: an evaluation of the effects on patient recruitment and data acquisition

PURPOSE

Clinical trials are time-consuming and require constant focus on data quality. Finding sufficient time for a trial is a challenging task for involved physicians, especially when it is conducted in parallel to patient care. From the point of view of medical informatics, the growing amount of electronically available patient data allows to support two key activities: the recruitment of patients into the study and the documentation of trial data.

METHODS

The project was carried out at one site of a European multicenter study. The study protocol required eligibility assessment for 510 patients in one week and the documentation of 46-186 data elements per patient. A database query based on routine data from patient care was set up to identify eligible patients and its results were compared to those of manual recruitment. Additionally, routine data was used to pre-populate the paper-based case report forms and the time necessary to fill in the remaining data elements was compared to completely manual data collection.

RESULTS

Even though manual recruitment of 327 patients already achieved high sensitivity (88%) and specificity (87%), the subsequent electronic report helped to include 42 (14%) additional patients and identified 21 (7%) patients, who were incorrectly included. Pre-populating the case report forms decreased the time required for documentation from a median of 255 to 30s.

CONCLUSIONS

Reuse of routine data can help to improve the quality of patient recruitment and may reduce the time needed for data acquisition. These benefits can exceed the efforts required for development and implementation of the corresponding electronic support systems.

CMDX©-based single source information system for simplified quality management and clinical research in prostate cancer

BACKGROUND

Histopathological evaluation of prostatectomy specimens is crucial to decision-making and prediction of patient outcomes in prostate cancer (PCa). Topographical information regarding PCa extension and positive surgical margins (PSM) is essential for clinical routines, quality assessment, and research. However, local hospital information systems (HIS) often do not support the documentation of such information. Therefore, we investigated the feasibility of integrating a cMDX-based pathology report including topographical information into the clinical routine with the aims of obtaining data, performing analysis and generating heat maps in a timely manner, while avoiding data redundancy.

METHODS

We analyzed the workflow of the histopathological evaluation documentation process. We then developed a concept for a pathology report based on a cMDX data model facilitating the topographical documentation of PCa and PSM; the cMDX SSIS is implemented within the HIS of University Hospital Muenster. We then generated a heat map of PCa extension and PSM using the data. Data quality was assessed by measuring the data completeness of reports for all cases, as well as the source-to-database error. We also conducted a prospective study to compare our proposed method with recent retrospective and paper-based studies according to the time required for data analysis.

RESULTS

We identified 30 input fields that were applied to the cMDX-based data model and the electronic report was integrated into the clinical workflow. Between 2010 and 2011, a total of 259 reports were generated with 100% data completeness and a source-to-database error of 10.3 per 10,000 fields. These reports were directly reused for data analysis, and a heat map based on the data was generated. PCa was mostly localized in the peripheral zone of the prostate. The mean relative tumor volume was 16.6%. The most PSM were localized in the apical region of the prostate. In the retrospective study, 1623 paper-based reports were transferred to cMDX reports; this process took 15 ± 2 minutes per report. In a paper-based study, the analysis data preparation required 45 ± 5 minutes per report.

CONCLUSIONS

cMDX SSIS can be integrated into the local HIS and provides clinical routine data and timely heat maps for quality assessment and research purposes.

Multilingual Medical Data Models in ODM Format: A Novel Form-based Approach to Semantic Interoperability between Routine Healthcare and Clinical Research

BACKGROUND

Semantic interoperability between routine healthcare and clinical research is an unsolved issue, as information systems in the healthcare domain still use proprietary and site-specific data models. However, information exchange and data harmonization are essential for physicians and scientists if they want to collect and analyze data from different hospitals in order to build up registries and perform multicenter clinical trials. Consequently, there is a need for a standardized metadata exchange based on common data models. Currently this is mainly done by informatics experts instead of medical experts.

OBJECTIVES

We propose to enable physicians to exchange, rate, comment and discuss their own medical data models in a collaborative web-based repository of medical forms in a standardized format.

METHODS

Based on a comprehensive requirement analysis, a web-based portal for medical data models was specified. In this context, a data model is the technical specification (attributes, data types, value lists) of a medical form without any layout information. The CDISC Operational Data Model (ODM) was chosen as the appropriate format for the standardized representation of data models. The system was implemented with Ruby on Rails and applies web 2.0 technologies to provide a community based solution. Forms from different source systems - both routine care and clinical research - were converted into ODM format and uploaded into the portal.

RESULTS

A portal for medical data models based on ODM-files was implemented (http://www.medical-data-models.org). Physicians are able to upload, comment, rate and download medical data models. More than 250 forms with approximately 8000 items are provided in different views (overview and detailed presentation) and in multiple languages. For instance, the portal contains forms from clinical and research information systems.

CONCLUSION

The portal provides a system-independent repository for multilingual data models in ODM format which can be used by physicians. It serves as a platform for discussion and enables the exchange of multilingual medical data models in a standardized way.

An eClinical trial system for cancer that integrates with clinical pathways and electronic medical records

BACKGROUND

Various information technologies currently are used to improve the efficiency of clinical trials. However, electronic medical records (EMRs) are not yet linked to the electronic data capture (EDC) system. Therefore, the data must be extracted from medical records and transcribed to the EDC system. Clinical pathways are planned process patterns that are used in routine clinical practice and are easily applicable to the medical care and evaluation defined in a trial protocol. However, few clinical pathways are intended to increase the efficiency of clinical trials.

PURPOSE

Our purpose is to describe the design and development of a new clinical trial process model that enables the primary use of EMRs in clinical trials by integrating clinical pathways and EMRs.

METHODS

We designed a new clinical trial model that uses EMR data directly in clinical trials and developed a system to follow this model. We applied the system to an investigator-initiated clinical trial and examined whether all data were extracted correctly. At the protocol development stage, our model measures endpoints based on clinical pathways with the same diagnosis. Next, medical record descriptions and the format of the statistical data are defined. According to these observations, screens for entry of data, which are used both in clinical practice and for study, are prepared into EMRs with an EMR template, and screens are prepared for data checks on our EMR retrieval system (ERS). In an actual trial, patients are registered and randomly assigned to a protocol treatment. The protocol treatment is executed according to clinical pathways, and the data are recorded to EMRs using EMR templates. The data are checked by a local data manager using reports created by the ERS. After edit checks and corrections, the data are extracted by the ERS, archived in portable document format (PDF) with an electronic signature, and transferred in comma-separated values (CSV) format to a coordinating centre. At the coordinating centre, the data are checked, integrated, and made available for a statistical analysis.

RESULTS

We verified that the data could be extracted correctly and found no unexpected problems.

LIMITATION

To execute clinical trials in our system, the EMR template and efficient ERSs are required. Additionally, to execute multi-institutional clinical trials, it is necessary to create templates appropriate for EMRs at all participating sites and for the coordinating centre to validate local templates and procedures.

CONCLUSION

We proposed and pilot tested a new eClinical trial model. Because our model is integrated with routine documentation of clinical practice and clinical trials, redundant data entries were avoided and the burden on the investigator was minimised. The reengineering of the clinical trial process would facilitate the establishment of evidence in the future.

Integrating clinical research with the Healthcare Enterprise: from the RE-USE project to the EHR4CR platform

BACKGROUND

There are different approaches for repurposing clinical data collected in the Electronic Healthcare Record (EHR) for use in clinical research. Semantic integration of "siloed" applications across domain boundaries is the raison d'être of the standards-based profiles developed by the Integrating the Healthcare Enterprise (IHE) initiative - an initiative by healthcare professionals and industry promoting the coordinated use of established standards such as DICOM and HL7 to address specific clinical needs in support of optimal patient care. In particular, the combination of two IHE profiles - the integration profile "Retrieve Form for Data Capture" (RFD), and the IHE content profile "Clinical Research Document" (CRD) - offers a straightforward approach to repurposing EHR data by enabling the pre-population of the case report forms (eCRF) used for clinical research data capture by Clinical Data Management Systems (CDMS) with previously collected EHR data.

OBJECTIVE

Implement an alternative solution of the RFD-CRD integration profile centered around two approaches: (i) Use of the EHR as the single-source data-entry and persistence point in order to ensure that all the clinical data for a given patient could be found in a single source irrespective of the data collection context, i.e. patient care or clinical research; and (ii) Maximize the automatic pre-population process through the use of a semantic interoperability services that identify duplicate or semantically-equivalent eCRF/EHR data elements as they were collected in the EHR context.

METHODS

The RE-USE architecture and associated profiles are focused on defining a set of scalable, standards-based, IHE-compliant profiles that can enable single-source data collection/entry and cross-system data reuse through semantic integration. Specifically, data reuse is realized through the semantic mapping of data collection fields in electronic Case Report Forms (eCRFs) to data elements previously defined as part of patient care-centric templates in the EHR context. The approach was evaluated in the context of a multi-center clinical trial conducted in a large, multi-disciplinary hospital with an installed EHR.

RESULTS

Data elements of seven eCRFs used in a multi-center clinical trial were mapped to data elements of patient care-centric templates in use in the EHR at the George Pompidou hospital. 13.4% of the data elements of the eCRFs were found to be represented in EHR templates and were therefore candidate for pre-population. During the execution phase of the clinical study, the semantic mapping architecture enabled data persisted in the EHR context as part of clinical care to be used to pre-populate eCRFS for use without secondary data entry. To ensure that the pre-populated data is viable for use in the clinical research context, all pre-populated eCRF data needs to be first approved by a trial investigator prior to being persisted in a research data store within a CDMS.

CONCLUSION

Single-source data entry in the clinical care context for use in the clinical research context - a process enabled through the use of the EHR as single point of data entry, can - if demonstrated to be a viable strategy - not only significantly reduce data collection efforts while simultaneously increasing data collection accuracy secondary to elimination of transcription or double-entry errors between the two contexts but also ensure that all the clinical data for a given patient, irrespective of the data collection context, are available in the EHR for decision support and treatment planning. The RE-USE approach used mapping algorithms to identify semantic coherence between clinical care and clinical research data elements and pre-populate eCRFs. The RE-USE project utilized SNOMED International v.3.5 as its "pivot reference terminology" to support EHR-to-eCRF mapping, a decision that likely enhanced the "recall" of the mapping algorithms. The RE-USE results demonstrate the difficult challenges involved in semantic integration between the clinical care and clinical research contexts.

Unlocking Data for Clinical Research - The German i2b2 Experience

OBJECTIVE

Data from clinical care is increasingly being used for research purposes. The i2b2 platform has been introduced in some US research communities as a tool for data integration and querying by clinical users. The purpose of this project was to assess the applicability of i2b2 in Germany regarding use cases, functionality and integration with privacy enhancing tools.

METHODS

A set of four research usage scenarios was chosen, including the transformation and import of ontology and fact data from existing clinical data collections into i2b2 v1.4 instances. Query performance was measured in comparison to native SQL queries. A setup and administration tool for i2b2 was developed. An extraction tool for CDISC ODM data was programmed. Interfaces for the TMF privacy enhancing tools (PID Generator, Pseudonymization Service) were implemented.

RESULTS

Data could be imported in all tested scenarios from various source systems, including the generation of i2b2 ontology definitions. The integration of TMF privacy enhancing tools was possible without modification of the platform. Limitations were found regarding query performance in comparison to native SQL and certain temporal queries.

CONCLUSIONS

i2b2 is a viable platform for data query tasks in use cases typical for networked medical research in Germany. The integration of privacy enhancing tools facilitates the use of i2b2 within established data protection concepts. Entry barriers should be lowered by providing tools for simplified setup and import of medical standard formats like CDISC ODM.

Concept and implementation of a computer-based reminder system to increase completeness in clinical documentation

PURPOSE

Medical documentation is often incomplete. Missing information may impede or bias analysis of study data and can cause delays. In a single source information system, clinical routine documentation and electronic data capture (EDC) systems are connected in the hospital information system (HIS). In this setting, both clinical routine and research would benefit from a higher rate of complete documentation.

METHODS

We designed a HIS-based reminder system which identifies not yet finalized forms and sends reminder e-mails to responsible physicians depending on escalation level. The generic concept to create reminder e-mail messages consists in database queries on not-finalized forms and generation of e-mail messages based on this output via the communication server. We compared completeness of electronic HIS forms before and after introduction of the reminder system three months each.

RESULTS

Completeness increased highly significantly (p<0.0001) for each form type (medical history form 93% (145 of 156 forms) vs 100% (206 forms), stress injection protocol 90% (142 of 157 forms) vs 100% (198 forms) and rest injection protocol 31% (45 of 147 forms) vs 100% (208 forms)). Forty-six reminder e-mails to the responsible study physician and 53 reminder e-mails to the principal investigator were sent to finish 2 medical history forms, 8 stress and 20 rest injection protocols. These 2 medical history forms were completed after 1 and 56 days. The median processing time of the stress injection protocols in the post-implementation phase was 18 days (range from 1 to 60 days). The median processing time of the rest injection protocols was 26 days (range from 5 to 37 days).

CONCLUSION

A computer-based reminder system to identify incomplete documentation forms with a notification and escalation mechanism can improve completeness of finalized forms significantly. It is technically feasible and effective in the clinical setting.

HIS-based Kaplan-Meier plots--a single source approach for documenting and reusing routine survival information

BACKGROUND

Survival or outcome information is important for clinical routine as well as for clinical research and should be collected completely, timely and precisely. This information is relevant for multiple usages including quality control, clinical trials, observational studies and epidemiological registries. However, the local hospital information system (HIS) does not support this documentation and therefore this data has to generated by paper based or spreadsheet methods which can result in redundantly documented data. Therefore we investigated, whether integrating the follow-up documentation of different departments in the HIS and reusing it for survival analysis can enable the physician to obtain survival curves in a timely manner and to avoid redundant documentation.

METHODS

We analysed the current follow-up process of oncological patients in two departments (urology, haematology) with respect to different documentation forms. We developed a concept for comprehensive survival documentation based on a generic data model and implemented a follow-up form within the HIS of the University Hospital Muenster which is suitable for a secondary use of these data. We designed a query to extract the relevant data from the HIS and implemented Kaplan-Meier plots based on these data. To re-use this data sufficient data quality is needed. We measured completeness of forms with respect to all tumour cases in the clinic and completeness of documented items per form as incomplete information can bias results of the survival analysis.

RESULTS

Based on the form analysis we discovered differences and concordances between both departments. We identified 52 attributes from which 13 were common (e.g. procedures and diagnosis dates) and were used for the generic data model. The electronic follow-up form was integrated in the clinical workflow. Survival data was also retrospectively entered in order to perform survival and quality analyses on a comprehensive data set. Physicians are now able to generate timely Kaplan-Meier plots on current data. We analysed 1029 follow-up forms of 965 patients with survival information between 1992 and 2010. Completeness of forms was 60.2%, completeness of items ranges between 94.3% and 98.5%. Median overall survival time was 16.4 years; median event-free survival time was 7.7 years.

CONCLUSION

It is feasible to integrate survival information into routine HIS documentation such that Kaplan-Meier plots can be generated directly and in a timely manner.

HIS-Based Support of Follow-Up Documentation - Concept and Implementation for Clinical Studies

OBJECTIVE

Follow-up data must be collected according to the protocol of each clinical study, i.e. at certain time points. Missing follow-up information is a critical problem and may impede or bias the analysis of study data and result in delays. Moreover, additional patient recruitment may be necessary due to incomplete follow-up data. Current electronic data capture (EDC) systems in clinical studies are usually separated from hospital information systems (HIS) and therefore can provide limited functionality to support clinical workflow. In two case studies, we assessed the feasibility of HIS-based support of follow-up documentation.

METHODS

We have developed a data model and a HIS-based workflow to provide follow-up forms according to clinical study protocols. If a follow-up form was due, a database procedure created a follow-up event which was translated by a communication server into an HL7 message and transferred to the import interface of the clinical information system (CIS). This procedure generated the required follow-up form and enqueued a link to it in a work list of the relating study nurses and study physicians, respectively.

RESULTS

A HIS-based follow-up system automatically generated follow-up forms as defined by a clinical study protocol. These forms were scheduled into work lists of study nurses and study physicians. This system was integrated into the clinical workflow of two clinical studies. In a study from nuclear medicine, each scenario from the test concept according to the protocol of the single photon emission computer tomography/computer tomography (SPECT/CT) study was simulated and each scenario passed the test. For a study in psychiatry, 128 follow-up forms were automatically generated within 27 weeks, on average five forms per week (maximum 12, minimum 1 form per week).

CONCLUSION

HIS-based support of follow-up documentation in clinical studies is technically feasible and can support compliance with study protocols.

Postapproval Development Options in COPD: A Case Study in Value-Based Healthcare Systems

BACKGROUND

Research and development activities in an era of globalization encounter a mosaic of providers, products, services, and intermediaries; regulatory and other government institutions; and consumers. The introduction of novel therapeutics into this environment mandates research programs that are relevant to the registration process, payers and purchasers, transparent pricing, and rule-driven business practices, while providing data relevant to marketing initiatives internationally.

OBJECTIVE

To outline an example for clinical development programs that incorporate the perspective of multiple stakeholders into a portfolio of study designs to provide optimal data platforms that can resonate with diverse recipients.

DISCUSSION

A contract research organization directly involved in the design, execution, and analysis of clinical trials for new drugs and devices across pharmaceutical and biotechnology companies provides a unique perspective regarding opportunities and challenges within the international clinical research environment. Drs Murphy, Antonini, and Lai, representing Worldwide Clinical Trials, utilize chronic obstructive pulmonary disease as a demonstration project exploiting its prevalence, direct and indirect costs, and the rapid infusion/diffusion of innovative therapy into practice as a rationale for focus, and illustrate methods of informing registration and technology assessments during a prototypical development process.

CONCLUSION

By virtue of its chronicity, prevalence, and pattern of healthcare utilization, chronic obstructive pulmonary disease provides an ideal case for illustrating the application of clinical trial methodology that can facilitate data evaluation through the prism of multiple stakeholders. Adding an international dimension exacerbates system complexity and serves to illustrate the breadth of issues that can be addressed within this therapeutic area.

A system dynamics analysis determining willingness to wait and pay for the implementation of data standards in clinical research

Background

Industry standards provide rigorous descriptions of required data presentation, with the aim of ensuring compatibility across different clinical studies. However despite their crucial importance, these standards are often not used as expected in the development of clinical research. The reasons for this lack of compliance could be related to the high cost and time-intensive nature of the process of data standards implementation. The objective of this study was to evaluate the value of the extra time and cost required for different levels of data standardisation and the likelihood of researchers to comply with these levels. Since we believe that the cost and time necessary for the implementation of data standards can change over time, System Dynamics (SD) analysis was used to investigate how these variables interact and influence the adoption of data standards by clinical researchers.

Methods

Three levels of data standards implementation were defined through focus group discussion involving four clinical research investigators. Ten Brazilian and eighteen American investigators responded to an online questionnaire which presented possible standards implementation scenarios, with respondents asked to choose one of two options available in each scenario. A random effects ordered probit model was used to estimate the effect of cost and time on investigators' willingness to adhere to data standards. The SD model was used to demonstrate the relationship between degrees of data standardisation and subsequent variation in cost and time required to start the associated study.

Results

A preference for low cost and rapid implementation times was observed, with investigators more likely to incur costs than to accept a time delay in project start-up. SD analysis indicated that although initially extra time and cost are necessary for clinical study standardisation, there is a decrease in both over time.

Conclusions

Future studies should explore ways of creating mechanisms which decrease the time and cost associated with standardisation processes. In addition, the fact that the costs and time necessary for data standards implementation decrease with time should be made known to the wider research community. Policy makers should attempt to match their data standardisation policies better with the expectations of researchers.

Toward an ontology-based framework for clinical research databases

Clinical research includes a wide range of study designs from focused observational studies to complex interventional studies with multiple study arms, treatment and assessment events, and specimen procurement procedures. Participant characteristics from case report forms need to be integrated with molecular characteristics from mechanistic experiments on procured specimens. In order to capture and manage this diverse array of data, we have developed the Ontology-Based eXtensible data model (OBX) to serve as a framework for clinical research data in the Immunology Database and Analysis Portal (ImmPort). By designing OBX around the logical structure of the Basic Formal Ontology (BFO) and the Ontology for Biomedical Investigations (OBI), we have found that a relatively simple conceptual model can represent the relatively complex domain of clinical research. In addition, the common framework provided by BFO makes it straightforward to develop data dictionaries based on reference and application ontologies from the OBO Foundry.

Routine data from hospital information systems can support patient recruitment for clinical studies

BACKGROUND

Delayed patient recruitment is a common problem in clinical studies. Hospital information systems (HIS) contain data items relevant for inclusion or exclusion criteria of these studies.

PURPOSE

We developed and assessed a system to support patient recruitment using HIS data.

METHODS

We developed a workflow integrated in our HIS to notify study physicians about potential trial subjects. Automatic HIS database queries based on inclusion and exclusion criteria for each clinical study are performed regularly and generate e-mail notifications via a communication server. Study physicians can verify eligibility with a specific HIS study module. The system performance was assessed with a survey addressing utility, usability, stability, change in recruitment rate, and estimated time savings.

RESULTS

During 10 months of operation, 1328 notifications were generated and 329 enrollments (25%) were documented for seven studies. Precision of alerts depends on availability of appropriate HIS items. Utility and usability were assessed as good, and stability as excellent. Users reported an increased patient recruitment rate for three studies. Three studies reported an estimated time saving of 10 min per recruited patient. The main perceived benefit was systematic identification of potentially eligible patients without time-consuming patient screening procedures in the different parts of the hospital.

LIMITATIONS

Notifications about potentially eligible patients depend on HIS data quality regarding inclusion/exclusion criteria, in particular, completeness, timeliness, and validity.

CONCLUSIONS

Routine HIS data can support patient recruitment for clinical studies by means of an automated notification workflow and efficient access to clinical data.

[Electronic health records and biomedical research]

The rapid progress in Web technology has led to the multiplication of health and research records for any given patient. Initiatives such as the personal medical record or the communicating cancer communicable records have recently been introduced. However, their primary aim is not for biomedical research. Several international groups of researchers are analyzing the appropriate role of the electronic health record as a support to biomedical research. The need to complete several distinct records for a given patient is a limiting factor, in view of the lack of medical and paramedical resources and the rising quality demands for both medical care and biomedical research. The impediments to "secondary reuse" of clinical data stored in electronic health records for biomedical research purposes are statutory, organizational, and technical. The international Integrating the Healthcare Enterprise (IHE) initiative has proposed a promising approach that uses an integration profile known as a Retrieve Form for Data Capture (RFD). A joint project by the North American Association of Cancer Registries and the Centers for Disease Control has made possible the automated transmission of pathology reports to the registries and thus limited the need for registry technicians to come copy these forms at the hospital.

HIS-based electronic documentation can significantly reduce the time from biopsy to final report for prostate tumours and supports quality management as well as clinical research

Background

Timely and accurate information is important to guide the medical treatment process. We developed, implemented and assessed an order-entry system to support documentation of prostate histologies involving urologists, pathologists and physicians in private practice.

Methods

We designed electronic forms for histological prostate biopsy reports in our hospital information system (HIS). These forms are created by urologists and sent electronically to pathologists. Pathological findings are entered into the system and sent back to the urologists. We assessed time from biopsy to final report (TBF) and compared pre-implementation phase (paper-based forms) and post-implementation phase. In addition we analysed completeness of the electronic data.

Results

We compared 87 paper-based with 86 electronic cases. Using electronic forms within the HIS decreases time span from biopsy to final report by more than one day per patient (p < 0.0001). Beyond the optimized workflow we observed a good acceptance because physicians were already familiar with the HIS. The possibility to use these routine data for quality management and research purposes is an additional important advantage of the electronic system.

Conclusion

Electronic documentation can significantly reduce the time from biopsy to final report of prostate biopsy results and generates a reliable basis for quality management and research purposes.