Last few years have seen data as well as data exchange emerging as the new currency in healthcare. Data sharing is a powerful force that is transforming conventional relationships in the healthcare marketplace as the global healthcare platform moves from being volume-based to quality-based. (1) Around 30% of the stored global data is generated within the healthcare industry. Also, a single patient normally generates about 80 MB of data every year in the form of imaging and electronic medical records (EMRs). The abundance of such data has substantial clinical, financial as well as operational value for the healthcare industry. (2) Moreover, such data could enable new value pathways, which would be worth more than $300 billion annually in reduced costs alone. (3)
However, at present, the essential value of these data has not been recognized to the fullest by the industry. Also, this value is realized only when the raw data is converted into knowledge that would lead the change in practice. It is also explained by more inclusive data sharing and insights from within the hospital or healthcare organization, health insurance partners and community stakeholders; and most importantly, by tailored partnering with individual patients to better understand chronic conditions, enhance adherence and compliance, boost self-care, and avoid costlier treatments at costlier sites of care within the hospital’s overall population base.2
Data is the basis for healthcare and medical research, therefore data sharing expedites the progress of research. Data sharing in research is widely discussed in the literature. Conversely, there are seemingly no evidence-based incentives that promote data sharing. In order to fully utilize the power of data and data sharing, providers, payers, and purchasers must be willing to work together to share cost and quality data across the entire healthcare system; instead of treating data as an exclusive asset. Patients routinely receive care and services from different providers, health systems, and health plans. In such instances, health data may not be consistent; which can create gaps in coverage leading to uneven, uncoordinated care of poor quality and high costs.1
Furthermore, in spite of numerous benefits, such as addressing emergencies on the global public health platform, data sharing is still not a common research practice. For example, the severe acute respiratory syndrome (SARS) disease was controlled within only 4 months after its appearance by a WHO-coordinated effort, which focused on extensive data sharing. Nevertheless, several studies have demonstrated as low rates of data sharing as 4.5% [as seen in the British Medical Journal (BMJ)] in the field of health care. The global spending on health and medical research is 85% of the total expenditure, out of which an estimated $170 billion is lost every year, leading to questions about the authenticity of scientific knowledge. Open data sharing should be vital to understand the source of ever expanding base of scientific knowledge. Open data will most certainly reduce waste in case of time, costs, and patient burden; eventually strengthening scientific knowledge by guaranteeing research integrity. (4)
The increasing gap between healthcare costs and outcomes can be attributed to poor management of research insights, poor usage of available evidence, and poor capture of care experience as well as valuable data, all leading to lost opportunities as well as resources, and potential harm to patients. To bridge this gap, the research and operational arms of healthcare can be used effectively to effectively harness data and encourage data sharing. (5)
Many approaches can be applied to encourage data sharing. While organisations are likely to favour an ‘opt-out’ model, expecting an opt-in approach based on active patient consent to be impractical that might yield low participation rate, patients must be conversant about the projected uses and benefits of sharing their data for research; which will generate awareness in data sharing and reduce the number of patients opting out. (6)
Another approach that can possibly boost data sharing would be the use of incentives. A recent systematic review has identified strategies that would facilitate data sharing practices among researchers. These strategies include the introduction to data systems, such as electronic laboratory notebooks and databases for data deposition in order to integrate a credit system through data linkage; group collaborations to use data attribution as an incentive; association among groups by means of workshops and agendas for data sharing; implementation of data sharing policies; and campaigns to promote data sharing. These strategies emphasize on the need of rewards to increase the rate of data sharing and the only form of incentive that has been successfully used is via data attribution and advertising on websites. Therefore, studies assessing the attribution efficacy and advertising as a form of credit are crucial. (4)
There are innumerable benefits of openness in research, such as verification of research findings, progress in health and medicine, increase in new insights as well as in research value, reduction in research waste, and promotion of transparency in research findings. However, there’s a lack of evidence-based incentives for researchers that hinders data sharing even in today’s evidence-based world. We have tried to suggest ways to encourage data sharing through the use of incentives. Using strategies like implementation of data systems can be adopted even by journals to use as reward for promoting reproducible and sharable research. (4,7)
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References
- Steele G. The culture of data sharing has to change. September, 2016.
- Huesch MD, Mosher TJ. Using it of losing it? The case of data scientists inside healthcare. May, 2017.
- Kayyali B, Knott D, Van Kuiken S. The big-data revolution in US healthcare: Accelerating value and innovation. McKinsey. April, 2013.
- Rowhani-Farid A, Allen M, Barnett AG, et al. What incentives increase data sharing in health and medical research? A systematic review. Research Integrity and Peer Review 2017; 2:4.
- Lee CH, Yoon H-J. Medical big data: promise and challenges. Kidney Research and Clinical Practice 2017; 36(1):3-11.
- New JP, Leather D, Bakerly ND, et al. Putting patients in control of data from electronic health records. BMJ 2018; 360:j5554
- Ioannidis JA, Khoury MJ. Assessing value in biomedical research: The PQRST of appraisal and reward. JAMA 2014; 312(5):483–4.
Written by: Ms. Tanvi Laghate
An excellent population health management approach considers multiple aspects of health, such as sufficiency of medical care, public health interventions, social and physical environments and related services, genetics, and individual behavior. (1) Data from multiple sources can be used to conduct population health management programs and to assess their value. In addition to these diverse data, data from big population health programs are driven by huge volume, high speed, and incoherent data flows. (2)
Maintaining a record of patients’ medical charts required a lot of physical labour before electronic medical/health records (EMRs/EHRs) became a routine. This physical work provided a greater chance of human error and overlooked information and eventually, irregular maintenance of records. There was a fair chance of 5 out of 15 charts for a clinic day to be unavailable at any given time, which ultimately led to wasted time, space, motion and frequent defects to care. (1)