While Electronic Medical Records (EMR) have been touted for decades as the golden ticket to exceptional patient care, research, and collaboration, the systems that support EMR have fallen short in delivering on that promise. The problem lies not so much in the systems themselves but in the quality of data residing within them, which comes with its own set of issues. Intake can be problematic, particularly when entered manually.
This can, and often does, lead to incorrect data – whether it be the result of typos, omitted information, or duplicate entry. In the healthcare arena, this is exacerbated thanks to the complicated and varied nomenclature used by hospitals, clinical care, and clinical research organizations. Imperfect – or dirty – data becomes even less useful when locked in EMR silos. Bottom line? When data is dirty and disconnected, it loses its inherent value.
To address these healthcare data issues and tap into data’s true significance, organizations must focus on data quality and harmonization. Only then can the hurdles posed by dirty data be overcome. Sentient software can help transform disconnected data into harmonized, integrated, and deeply searchable data.
How to clean it up to unlock drug discovery, collaboration, research, personalized medicine, and more.
From isolated medical data to actionable information
Real-world clinical and healthcare research data is highly valuable yet also remarkably complex and diverse. Clinical and life science research data can use different terminologies, causing even the most disciplined clinical practices to generate dirty data. Tools for data quality improvement can help optimize that data. Look for solutions that utilize master reference data sources, reference ontologies, and machine reasoning to accurately identify and establish common terminology, units, and formatting.
Smart, sharp data quality profiling, verification, de-duping, and enrichment tools apply artificial intelligence (AI), machine reasoning, and advanced semantic strategies to uncover connections within complex, changing data. These tools can be harnessed via API or server-based workflows to unlock the true value hidden in complex, changing clinical data. The result? Previously under-valued data is transformed into research and application-quality datasets.
Through the application of ontologies, organizations can define semantic concepts and the relationships necessary in applying machine reasoning and pattern recognition. Integrated data enables the exploration of pattern identification and interest; this can be beneficial in further data curation or advanced decision support methodologies.
No more dirty data: From clean to enriched to FAIR compliant
Autocompleting and validating drug data during entry saves time, mitigates data entry errors, and avoids confusion. Validation also improves pharmacometrics and healthcare informatics by verifying pharmaceutical names, variants, and spellings against a pharmacopeia. Data quality and machine reasoning can be used to check and verify drug names and find alternate names and variations through inferred linkage.
Drug data should be enriched with additional information about a drug or list of medications, such as dose, route, disease indication, and more. A data repository can be enhanced regularly with new drug information, issued warnings, recent discoveries, DDI (Drug-Drug Interaction), and ADE (Adverse Drug Effects).
Comprehensive and integrated reference data, along with terminology and ontologies for drugs, diseases, proteins, and genes, should also be EMR imperatives. They allow organizations to efficiently normalize data to preferred terminologies, map data to useful research ontologies and connect it to additional information from groups and standards such as FDA, NDC, UMLS, and others.
If mandated to share or collaborate around clinical and published study data, it is necessary to align with collaboration and interoperability standards. To meet FAIR guidelines, data must be findable, accessible, interoperable, and reusable – all the while maintaining regulatory compliance for protected health information, contractual and business compliance, security, and confidentiality. You can more easily and effectively publish and share (or collaborate around) your data by meeting FAIR guidelines and all required standards compliance for data publication and sharing.
Realizing the promise of EMR
Clean and integrated clinical data environments allow hospitals and clinics to improve care at lower costs, share data with leading research communities, and realize substantial revenue from data brokering collaborations and more efficient clinical trials.
With reliable data, medical and research professionals have what they need to bring EMR objectives to fruition. Physicians and hospitals can more intelligently, and cost-effectively treat patients via a comprehensive “360-degree view” of the patient. A clinician or clinical researcher can search for information that is meaningfully “linked” across previously connected data resources. Clinical, administrative, and scientific users benefit from the ability to easily ask virtually any question across all permitted data within a clinic, garnering results in seconds. Clinicians can avoid losing valuable face time with patients while poring through different, disconnected software and printed reports. Researchers can ask critical questions, getting clear reports back in seconds rather than months.
In addition to identifying and qualifying clinical trials participants more quickly, a clean, integrated data resource makes it possible to engage data-driven partnerships with biotechnology and pharmaceutical companies. Research quality integrated clinical data is in high demand within the biotechnology and pharma industries.
From the moment data enters a system, it starts to degrade. The only way around it is through a strong commitment to data quality and harmonization practices. With a laser focus on keeping data clean and current, organizations have the potential to improve patient care and support precision medicine research, engage in high paying pharmaceutical research partnerships, realize new intellectual property from clinical practice data, capture and report research quality clinical trials data at a lower cost, and achieve new revenue by making the most of clinical data.
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