There are humungous quantities of data existing in healthcare; data from all kinds of sources, such as clinical, patient, payer, R&D, pharmacy as well as revolutionary technologies that are being quickly embraced, for e.g. data from wearable devices. According to a report by International Data Corporation (IDC), (1) the volume of healthcare data which was observed to be around 153 exabytes in 2013 is estimated to reach around 2,314 exabytes in the year 2020. Therefore, integrating data from all types of diverse sources and clinical systems is a fundamental challenge for any healthcare entity in order to enhance patient care and performance indicators. (2)
It’s obvious that these huge amounts of health data are essential for betterment of both the cost as well as the quality aspects of care. Also, analyses of these data can provide significant insights for patients and researchers. However, methods to merge data from multiple formats and sources ranging across various systems used within clinics are still unclear. Data quality and accessibility provided by these systems can vary to a great extent. The healthcare industry has been traditionally observed to embrace new technologies; however, it lags behind while handling data, particularly data sharing and integration. To add to the practical challenges of data integration processes, compliance and capability to join forces with all the healthcare stakeholders also faces problems. As a consequence, data collection, storage, integration, and analysis make up for complicated processes. (2)
There are some specific underlying concerns surrounding multiple, un-integrated data sources, viz. lack of broad view into enterprise-wide data as well as data standardization and governance, and matching patients to care events. Lack of broad view can impose challenges resulting in time consuming and expensive procedures during development of meaningful internal and external reports, like quality and patient safety regulatory and accreditation reporting. It may also hamper efforts to identify and prioritize opportunities to reduce costs, while improving care and patient experience. Lack of data standardization and governance can hamper performance of important analytics owing to multiple data sources, definitions and terms. Last but not the least, it is crucial to match patients accurately to their respective care events across multiple sites of care, which can be a complicated process. (3,4)
There is no doubt that the Healthcare systems undoubtedly require effective data integration tools and greater level of flexibility when handling data, typically from multiple sources. The standards implemented in many countries recently have been intended for healthcare data integration and unification. For instance, in the USA the Health Information Technology (HITECH) Act (5) offers incentive payments to health care providers implementing certified EHR technology while showing meaningful use of that technology. HIPAA standards provide healthcare data protection; while HL7 standards allow clinical and administrative data communication between software applications used by various healthcare providers. (6)
In order to gain patient insights, integration of data from multiple sources can prove to be beneficial. One way to facilitate data integration can be incorporating data warehouses [enterprise data warehouses (EDWs)], which can facilitate easy data mining in case of faster, major data initiatives. These methods can pull in and push out data with just one interface. Furthermore, data governance policies focusing on data standardization, advances in data reporting and further education and communication need to be in place in order to make changes in how data is to be collected, defined, and consumed. By integrating health data with financial and cost data to track patient encounters across multiple care locations and information systems, it is easier for health systems to compare patient quality and cost, i.e. comprehending the exact process of ‘value’ delivery. This insight is the difference between surviving and thriving in the new value based purchasing environment. (4)
Clinical data integration from multiple sources can provide a wide-ranging perspective across care delivery systems. Health systems can easily carry out reporting while employing quality improvement initiatives, such as analytical care variation and measuring implementation of evidence-based guidelines. (4)
To sum it all up, multiple data integration can obviously facilitate electronic exchange of information, while also reducing the costs and intricacies of building interfaces between different systems; thus proving valuable patient insights. The foundation of the healthcare industry’s data-sharing conundrum is data interoperability. Genuinely integrated systems must be easily understood by users, i.e. these systems must be able to exchange data and consequently put it forward through inclusive and user friendly interface.
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References
- Corbin K. How CIOs can prepare for healthcare ‘data tsunami’. December, 2014.
- Healthcare data integration: How to combine data from multiple sources.
- Managing the integrity of patient identity in health information exchange. American Health Information Management Association. 2009.
- Turning Data from Five Different EHR Vendors into Actionable Insights. Health Catalyst.
- Health Information Technology (HITECH Act). 2009.
- Summary of the HIPAA Privacy Rule.
Healthcare outcomes are defined as the changes observed and/or recorded in health status of individual or population patient/s usually due to an intervention, measures or specific healthcare investment. (1) The goal is to save the lives, shorten hospital stays and build healthier communities relying on preventative measures. (2) The fundamental steps of improving outcomes are measuring, reporting and analysing the outcomes. The efficient synthesis, organization and analysis of healthcare data offer the healthcare providers and other healthcare stakeholders with systematic and insightful treatment, measures and diagnosis. This may lead to higher patient care quality and better outcomes at lower costs.
Money drives human behavior so much so that it’s rarely thought about. Two-for-one or super-saver deals on anything right from movie tickets to clothing and other commodities compel the consumer and work–life choices; then why not health behavior? It’s a known fact that individuals smoke or drink less when the government increases tax on tobacco or alcohol. But do they react the same way when financial incentives are provided as part of targeted, personalized health promotion programs? (1)
The biologics market is rapidly growing, at almost twice the rate of pharma industry. Estimates are such that 7 of the top 10 global medicines by spending will be a biologic within the next 5 years. To add to this, a new class of biologics, known as “biobetters”, is being introduced that could compete with biosimilars for market share. While the term “biosimilar” is used for a drug that is highly similar to its reference, with no clinically significant differences from the originator product, the term “biobetter” applies to a therapy resulting from intentionally altering a biologic product improving its clinical effects, requiring less frequent administration, or enhancing tolerability. (1)
Missing data are a big concern in any research project and are often unavoidable in spite of investigators’ best efforts. Missing outcomes have two effects: reduced precision and power, and bias. Also, the loss of precision is inevitable, except the possible use of the available data; e.g. to be sure not to exclude from the analysis individuals who dropped out before the end of the study but who nevertheless reported intermediate values of the outcome. However, the statistician can aim to reduce bias through suitable choice of an analysis. (1)