Development of an advanced database for clinical trials integrated with an electronic patient record system

Newsham, Alex; Johnston, C.F.; Hall, Geoff; Leahy, Michael; Smith, Adam B.; Vikram, A.; Donnelly, Aine M.; Velikova, Galina; Selby, Peter J.; Fisher, Susan E.

doi:10.1016/j.compbiomed.2011.04.014

Cited by 30 publications

(20 citation statements)

References 6 publications

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“…Hospitals NHS Trust (LTHT) (16,17). We studied patients with two common malignancies, breast and colorectal cancer, receiving two frequently used chemotherapy regimens; adjuvant epirubicin and cyclophosphamide (EC90) and palliative oxaliplatin and modified de The system has recently been extended to collate data on all patients whose care has been delivered at LTHT since 2000 and contains the records of more than 2.39 million patients.…”

Section: Introductionmentioning

confidence: 99%

Process mining routinely collected electronic health records to define real-life clinical pathways during chemotherapy

Baker¹,

Dunwoodie

Jones

et al. 2017

International Journal of Medical Informatics

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Section: Introductionmentioning

confidence: 99%

Process mining routinely collected electronic health records to define real-life clinical pathways during chemotherapy

Baker¹,

Dunwoodie

Jones

et al. 2017

International Journal of Medical Informatics

Self Cite

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“…In many Yorkshire hospitals, the EPR is patient pathway manager (PPM). Patient pathway manager exports clinical data to the regional registry to assist with registration and, in addition to medical data, contains a research/trials database, in which studies are listed, consented patients ‘tagged' as such, and associated study events recorded (e.g., date consented; Newsham et al , 2011). To enable ePOCS to receive information via PPM, local hospital IT adjusted the PPM → registry data export to include research information, and to run more frequently (i.e., nightly).…”

Section: Resultsmentioning

confidence: 99%

Integrating cancer survivors' experiences into UK cancer registries: design and development of the ePOCS system (electronic Patient-reported Outcomes from Cancer Survivors)

et al. 2011

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Background:Understanding the psychosocial challenges of cancer survivorship, and identifying which patients experience ongoing difficulties, is a key priority. The ePOCS (electronic patient-reported outcomes from cancer survivors) project aims to develop and evaluate a cost-efficient, UK-scalable electronic system for collecting patient-reported outcome measures (PROMs), at regular post-diagnostic timepoints, and linking these with clinical data in cancer registries.Methods:A multidisciplinary team developed the system using agile methods. Design entailed process mapping the system's constituent parts, data flows and involved human activities, and undertaking usability testing. Informatics specialists built new technical components, including a web-based questionnaire tool and tracking database, and established component-connecting data flows. Development challenges were overcome, including patient usability and data linkage and security.Results:We have developed a system in which PROMs are completed online, using a secure questionnaire administration tool, accessed via a public-facing website, and the responses are linked and stored with clinical registry data. Patient monitoring and communications are semiautomated via a tracker database, and patient correspondence is primarily Email-based. The system is currently honed for clinician-led hospital-based patient recruitment.Conclusions:A feasibility test study is underway. Although there are possible challenges to sustaining and scaling up ePOCS, the system has potential to support UK epidemiological PROMs collection and clinical data linkage.

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“…The PPM EHR contains essentially episodic (episode of care) records for 3 million patients [33,34]. Initial work data mining cancer care pathways in PPM surfaced concerns with the veracity of data but also fascinating insights into patterns of care and their potential link with health outcomes [35].…”

Section: Big Data Research Infrastructurementioning

confidence: 99%

NETIMIS: Dynamic Simulation of Health Economics Outcomes Using Big Data

2016

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Many healthcare organizations are now making good use of e-health record (EHR) systems to record clinical information about their patients and the details of their healthcare. Electronic data in EHRs is generated by people engaged in complex processes within complex environments and their human input, albeit shaped by computer systems, is compromised by many human factors. This data is potentially valuable to health economists and outcomes researchers but is sufficiently large and complex enough to be considered part of the new frontier of "big data". This paper describes emerging methods that draw together data mining, process modeling, activity based costing and dynamic simulation models. Our research infrastructure includes safe links to Leeds hospital's EHR with 3 million secondary and tertiary care patients. We created a multi-disciplinary team of health economists, clinical specialists, data and computer scientists and developed a dynamic simulation tool called NETIMIS (www.netimis.com) suitable for visualization of both human-designed and data-mined processes which can then be used for "what-if" analysis by stakeholders interested in costing, designing and evaluating healthcare interventions. We present two examples of model development to illustrate how dynamic simulation can be informed by big data from an EHR. We found the tool provided a focal point for multi-disciplinary team work to help them iteratively and collaboratively "deep dive" into big data. Key Points for Decision Makers Big data can be defined as a data at a scale where research become uncomfortable -the point where existing methods are unable to unlock the value in the data.  Electronic health records (EHR) are a type of big data that will be valuable to future health economists and outcomes researchers but the data is messy, it is the human product of large numbers of individuals working in complex environments. Solutions have yet to emerge.  We found that the use of a dynamic simulation tool provided an effective focal point for multidisciplinary investigations into the messy world of EHR big data and the development of valuable insights.2

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Development of an advanced database for clinical trials integrated with an electronic patient record system

Cited by 30 publications

References 6 publications

Process mining routinely collected electronic health records to define real-life clinical pathways during chemotherapy

Process mining routinely collected electronic health records to define real-life clinical pathways during chemotherapy

Integrating cancer survivors' experiences into UK cancer registries: design and development of the ePOCS system (electronic Patient-reported Outcomes from Cancer Survivors)

NETIMIS: Dynamic Simulation of Health Economics Outcomes Using Big Data

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