Healthcare Analytics is the set of activities or actions undertaken as a result of data collected from different areas within healthcare. Healthcare 2.0 has been defined variously as including social media, user-generated content, and cloud-based and mobile technologies. Some Health 2.0 proponents see these technologies as empowering patients to have greater control over their own health care and diminishing medical paternalism. Healthcare 2.0 can be said to be a step ahead of normal healthcare analytics. These areas are four in number and they are Claims and cost data, pharmaceutical and research development (R&D) data, clinical data (electronic medical records) and patient behavior and sentiment data(i.e patient behaviors and preferences). Put another way, involves the activities that are undertaken or actions carried out as a result of data collected from different areas within healthcare. These areas are four in number and they are Claims and cost data, pharmaceutical and research development (R&D) data, clinical data (electronic medical records) and patient behavior and sentiment data(i.e patient behaviors and preferences). The healthcare analytics model has picked up momentum in the past and it’s expected to grow exponentially in the coming years.
In the late 2000s, several commentators used Health 2.0 as a moniker for a wider concept of system reform, seeking a participatory process between patient and clinician: “New concept of health care wherein all the constituents (patients, physicians, providers, and payers) focus on health care value (outcomes/price) and use competition at the medical condition level over the full cycle of care as the catalyst for improving the safety, efficiency, and quality of healthcare” Health 2.0 defines the combination of health data and health information with (patient) experience, through the use of ICT, enabling the citizen to become an active and responsible partner in his/her own health and care pathway. Health 2.0 is participatory healthcare. Enabled by information, software, and communities that we collect or create, we the patients can be effective partners in our own healthcare, and we the people can participate in reshaping the health system itself.
Definitions of Medicine 2.0 appear to be very similar but typically include more scientific and research aspects—Medicine 2.0: “Medicine 2.0 applications, services and tools are Web-based services for healthcare consumers, caregivers, patients, health professionals, and biomedical researchers, that use Web 2.0 technologies as well as semantic web and virtual reality tools, to enable and facilitate specifically social networking, participation, apomediation, collaboration, and openness within and between these user groups.
Healthcare 2.0 is also complementary to both predictive and comparative analytics
Predictive Analytics: A major drawback will be that predictive analytics fails to include outcomes. Even though predictive analytics supports risk stratification through case management and other means, without outcomes data prediction is technically nonexistent. Many healthcare organizations and health researchers do not understand how predictive analytics truly works before diving head first into it. There is a general misunderstanding of its technicalities and program specifics. Without protocol and patient-specific outcomes data, predictive analytics is largely just predicting readmissions with no actual solution in sight.
Comparative Analytics: Comparative data analytics has existed for quite some time now in the healthcare industry but there hasn’t been much improvement. Healthcare quality and cost continues to deteriorate and increase respectively with no hope of redemption in sight. Comparative analytics has its advantages though but they might not be enough to drive improvements and advancements in a healthcare organization. There are too many variables and variations in healthcare delivery and data to make comparative analytics as valuable as would be considered ideal.