The patient’s experience-related to his or her symptoms, goals of care, and overall quality of life-has traditionally been captured in the health record as described by the patient to the provider and then interpreted and documented by clinicians. Within this big-data vision, EHRs, administrative data, public health records, biometric sensors, and many other data sources are integrated to create a more unified story of health and health care.Ī key feature of and central linking concept within these data is the role of the individual patient. The increasingly central role of the EHR, coupled with a renewed focus on the importance of the voice and experience of the patient, creates a clear opportunity to generate meaningful data that contribute to the “big data” vision (the often-discussed “3 Vs” of big data are the high volume of data at a high velocity, and drawing on a variety of data). 5 – 7 Leveraging these data requires a wide spectrum of skill sets, including principles of scientific inquiry, data collection, data aggregation, health informatics, analytics, evidence implementation, and incremental learning.ĭata captured at the point of clinical care contribute important data elements in support of this goal. Linking Big Data, Comparative Effectiveness Research, And PatientsĮlectronic data capture supports health services research, comparative effectiveness research, health care redesign, personalized medicine, and the assessment of health care quality. For example, academic and community clinical data sources were recently used to analyze treatment patterns and the patient experience for over six hundred people with kidney cancer. There are numerous examples in the literature in which data generated in routine clinical care are used to better understand the quality and results of the care being delivered. However, the widespread implementation of electronic health records (EHRs) and novel informatics approaches are creating new resources for data analysis. In the past, research on care has relied on claims data. 2 Aggregating and analyzing real-world data directly from clinical care to support the evaluation of therapies is a compelling alternative to randomized controlled trials. It can take more than a decade for a trial to progress from the idea stage to actionable information, and cost and complexity mean that many questions will never be addressed with such trials. However, the generalizability of data from these trials to larger, more heterogeneous populations to determine treatment effectiveness is problematic. The randomized controlled trial is the gold standard for determining treatment efficacy. Comparative effectiveness research seeks to develop evidence that can aid patients, caregivers, providers, payers, and policy makers in assessing interventions to determine what is the most appropriate and cost-effective care. In this era of increasing availability of treatment options for many health conditions, there is a pressing need to develop an evidence base for the delivery of the right treatment to the right patient at the right time. We conclude that leveraging the emerging wealth of “big data” being generated by patient-facing technologies such as systems to collect patient-reported outcomes data and patient-worn sensors is critical to developing the evidence base that informs decisions made by patients, providers, and policy makers in pursuit of high-value medical care. We observe that the key to high-quality patient-generated data is to have immediate and actionable data so that patients experience the importance of the data for their own care as well as research purposes. We also review in brief federal and medical society efforts to create new streams of patient-generated data for clinical and research use. In this article we assess the need for, uses of, and strengths and weaknesses of patient-generated data. Methods of generating evidence for comparative effectiveness research provide opportunities to engage patients and understand their experiences with illness and its treatment. To conduct this type of research and to inform health care delivery, data about the impact of interventions on patient outcomes are needed. The goal of comparative effectiveness research is to assess medical therapies and allow patients, health care providers, payers, and policy makers to make evidence-based decisions about the most appropriate therapies in routine clinical practice.
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