Using data and technology to improve healthcare ecosystem

By IT Advisory, 3rd Eye Advisory Ltd
Using data and technology to improve healthcare ecosystem

The healthcare industries are facing drastic changes or witnessing a fundamental transformation from being a volume-based business to an outcome based business. There is a dire need of understanding the evolving role of analytics in healthcare which is playing a vital role in improving patients' health. Traditional methods are no longer useful in releasing the appropriate information about patients' behaviour, sentiments and in evaluating the most effecting treatment for a particular disease. The cost dynamics of the industry are also changing with the improved longevity of people, higher number of patients with chronic illness, and increase in geriatric population. At the same time, the industry has been lagging behind the others in adopting technology- enabled process improvements. The nature of the healthcare industry itself also creates challenges: while there are many players, there is no way to easily share data among different providers or facilities, partly because of privacy concerns and even within a single hospital, payor, or pharmaceutical company, important information often remains within one group or department because organizations lack procedures for integrating data and communicating findings.

Technological advances:

According to Gartner, healthcare analytics is a rapidly emerging phenomenon with huge future potential. The traditional obstacles of compiling, storing, and sharing information securely are overcoming through technologic advances. In addition to facilitating longitudinal studies and other research, technological advances have made it easier to “clean” data and preserve patient privacy.

The new programs can readily remove names and other personal information from records being transported into large databases, complying with all Health Insurance Portability and Accountability Act (HIPAA) patient- confidentiality standards. Some computer systems can even examine information across all data pools- an important feature since there are special combinations that can provide more insights than any individual data set. For example, claims data may show that a patient has tried three treatments for cancer, but only the clinical data show us which treatment was effective in shrinking the tumour. As another example, personal behaviour information may show that a patient is taking fewer trips outside the house or looking up information on side effects online, both of which could suggest physical problems or be early indicators of an illness requiring early intervention to prevent a more serious medical episode. But only clinical data will confirm whether the behaviours were truly linked to illness.

Impact of big data on the healthcare system:

Big data is characterised by large volumes of high velocity, complex and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management and analysis of the information. Analytical techniques can be applied to the vast amount of existing medical data to evaluate a deeper understanding of outcomes. Individual and population data would help each physician and his/her patient during the decision-making process and help determine the most appropriate treatment option for that particular patient.

Big data analytics is evolving into promising fields for providing insights from very large data sets and improving outcomes while reducing costs. It has transformed the discussion of what is appropriate or right for a patient and for the healthcare ecosystems. In keeping with these changes, a holistic patient centred framework is generated that considers five key pathways to value, based on the concept that value is derived from the balance of the healthcare spend and patient impact (outcomes).

The 4 "Vs" of big data analytics in healthcare:
The analytics associated with big data is described by three primary characteristics i.e. Volume, Velocity, Variety and Veracity (introduced by practitioners).

The already daunting volume of existing healthcare data includes personal medical records, radiology images, clinical trial data and population data genomic sequences, etc. Newer forms of big data such as 3D imaging, genomics are also initiating the exponential growth. The enormous variety of data- structured, unstructured and semi-structured is a dimension that makes healthcare data both interesting and challenging. Advances in data management, virtualization and cloud computing are facilitating the development of platforms for more effective capture, storage and manipulation of large volume of data. Data is accumulated in real-time and at a rapid pace or velocity.

Some practitioners and researchers have introduced a fourth characteristics, veracity, or 'data assurance'. Data quality issues are of acute concern in healthcare for two reasons: life or death decisions depend on having accurate information, and the quality of healthcare data (unstructured data) is highly variable and often incorrect.

Datasets that exist within the healthcare ecosystem:

  • Pharmaceutical/ device companies, contract research organizations, drug research organizations
  • Healthcare provider clinical data (hospitals, physician practices, laboratories, etc.)
  • Patients behaviour and sentiment data

The healthcare big data business cases that have seen tangible results:

  • Traditionally, physicians used to practice the event-based medicine and apply the same sequence of tests to all patients who admitted into the emergency departments with the similar symptoms. This can be considered as efficient but it is rarely effective.
  • Detailed analysis of patient data helps caregivers take an evidence-based approach to medicine.
  • Physician can understand patient hereditary genotype for effectively managing the disease.
  • By analysing detailed imaging tests, and case histories, physicians are able to extrapolate the likely course of the disease's progression.
  • Study the use of medications in very large populations to determine the efficacy and the adverse effects of the drug.
  • Fraud analytics with the power of using predictive modelling and business rules to score claims based on a number of known risk factors. This helps reduce the claims settlement time.

The most important part of analytics is managing and utilizing data. The healthcare industry is gathering information along with the rise of medical imaging technology (images), digital multimedia (video, audio) and the real time data to monitor data vital. Social media is enabling communication between patients, providers, and communities which is potentially becoming an important source for Big Data and thus improving healthcare ecosystem.

Big Data in healthcare is poised to change the ecosystem. While it is still early in the game, there are many ways by which Big Data is currently being leveraged to create value across healthcare. Nowadays, we can easily access the diverse medical data in various healthcare organizations (payers, providers, pharmaceuticals, and regulatory). In the coming time, with the advancement of newer models of analytics and more strategic data collaboration between healthcare organizations, patients will be able to see reduced cost for better care and visibility to a variety of health care information.

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Article by: IT Advisory, 3rd Eye Advisory Ltd
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