Big Data to Predictive Analytics – Future of Healthcare

New Delhi, September 22, 2013: The importance of healthcare to individuals and governments and its growing costs to the economy have contributed to the emergence of healthcare as an important area of research for scholars in business and other disciplines. Information Technology has much to offer in managing healthcare costs and in improving the quality of care.

Today, healthcare executives are under tremendous pressure to address a host of different challenges: medical errors, rising costs, inconsistent quality, inefficiency, declining doctor satisfaction, and mounting staff shortages. Dealing with these issues will ultimately lead to better healthcare, but the process appears as complex and overwhelming as the challenges themselves. Information – or lack of it – is a big part of today’s healthcare problems. Accordingly, Information Technology should be a big part of the solution.

 

Healthcare stakeholders can now have access to promising new threads of knowledge.  This information is in the form of ‘’Big Data’’, so called not only for its sheer volume but for its complexity, diversity and timeliness. Though, this Big Data revolution is in its early days and is still a fuzzy concept, data is growing and moving faster than healthcare organizations can consume it. More than 90% of medical data is clinically relevant, yet unstructured.

 

This data resides in multiple places like individual EMR, lab and imaging systems, physician notes, medical correspondence, claims, CRM systems and finance. Getting access to this valuable data and factoring it into clinical and advanced analytics is critical to improving care and outcomes, incentivizing the right behavior and driving efficiencies. Interoperability and Health Information Exchanges (HIE) could connect number of hospitals and may have records for millions of patients, which could be used by physicians.

 

No algorithm, that exists today, is able to predict the impending stroke of a 34-year old patient who is a competitive tri-athlete with no family history of cardiovascular disease.

Predictive analytics requires analysis / real-time analysis of the structured big data which could make healthcare proactive and not reactive. Predictive analytics can improve results for both the patient and businesses participating in the complex healthcare market. It simulates PRO (Patient Reported Outcomes) for care quality improvement / outcomes and could predict high risk patients for ACO (Accountable Care Organization) and hospitals along with simulating connected health consumer and recommend technology interventions that drive healthy behavior change.

 

 

Predictive analytics and the potential benefits can be achieved by using it to gain insights into health care Big Data and how data analytics can be used for predictive analytics in changing the connotation of healthcare. Predictive analytics will drive efforts to improve clinical quality and financial performance in health care and tie costs to the outcomes. Data becomes paramount with DNA analysis and genomic surveillance being used to predict best treatment plans, specific drug value, and

 

 

interventions prior to development of chronic illness. One must consider genetically-engineered drugs for the patient’s specific genetic complement.

 

Privacy and security is a core issue when dealing with healthcare information. One of the top concerns of all e-Health programs is how the privacy and security of personal health data can be guaranteed. The IT industry is able to provide better, safer and more reliable solutions, however, security breaches do not require just technical solutions, but also laws, detection of violations, enforcement and punishment. The European Union already is enforcing strict medical data security standards and the North American market with the health Insurance Portability and Accountability Act (HIPAA) is also demanding improved security and confidentiality for healthcare transactions.

 

 

Main Applications of IT in Healthcare:

           
In a general sense, the care of a patient comes together through the focus of many clinical disciplines—medicine, nursing, pharmacy, etc. Although the work of the various disciplines sometimes overlaps, each has its own primary focus, emphasis, and methods of care delivery. Each discipline’s work is complex in and of itself, and collaboration among disciplines adds another level of complexity. In all disciplines, the quality of clinical decisions depends in part on the quality of information available to the decision-maker.          

The emergence of the electronic medical record (EMR) as the key system for providing clinicians with an integrated view of clinical information in hospitals creates the requirement for ancillary systems, such as those of laboratories, radiology and pharmacy to accept orders from the EMR and replicate clinical data to it as components of an integrated clinical database.

 

Radio frequency identification (RFID): This technology tracks patients throughout the hospital, and links lab and medication tracking through a wireless communications system.  It is neither mature nor widely available, but may be an alternative to bar coding.

 

Picture archiving and communications system (PACS): This technology captures and integrates diagnostic and radiological images from various devices (e.g., x-ray, MRI, computed tomography scan), stores them, and disseminates them to a medical record, a clinical data repository, or other points of care.

 

Clinical decision support system (CDSS): CDSS provides physicians and nurses with real-time diagnostic and treatment recommendations.  The term covers a variety of technologies ranging from simple alerts and prescription drug interaction warnings to full clinical pathways and protocols.

 

Telehealth / Telecare projects: These include implementations in areas such as vital signs monitoring, mobile disease monitoring, remote diagnosis and treatment or home care support tools. A broad usage of these technologies can support significant cost savings and quality improvements and a lot of focus is going into this area currently         

 

 

 

 

Need & Importance of Standardization:

 

 

1) ICD 11

 

Aim of WHO is to produce a family of closely retorted documents on diagnosis so as to meet the requirements of persons working in a wide variety of settings. In line with advances in Information technology, ICD-11 (International Classification of Diseases) will be used with electronic health applications and information systems. ICD 11 is due to come in practice in the year 2015.

 

ICD-11 revision process allows for collaborative web-based editing that open to all interested parties. To assure quality it will be peer reviewed for accuracy and relevance.

• Definitions, signs and symptoms, and other content related to diseases will be defined in a structured way so it can be recorded more accurately
• It is compatible with electronic health applications and information systems.

 

2) HL7

 

Health Level Seven is a standard for exchanging information between medical applications. This standard defines a format for the transmission of health-related information.

 

Information sent using the HL7 standard is sent as a collection of one or more messages, each of which transmits one record or item of health-related information. Examples of HL7 messages include patient records, laboratory records and billing information.

 

 

Stakeholders

 

Government including Regulatory bodies, Healthcare Service Providers, Research Institutes, Consultants, Financial Institutes and Technology Providers. — CCI Newswire