According to a survey, big data analytics and informatics skills are the ‘prospective’ two most important resources, to be used by the healthcare providers in their decision making and data management processes. The health information sector is experiencing some major changes, like, for example, the conversion of paper health records into electronic health records (EHR). Health Information Management (HIM) is also evolving, to be utilizing big data in population health management, predictive analytics, clinical quality enhancements, etc. Health Information Management, needs to view data as an asset, in order to utilize the data, in the best possible way. Read more at: http://healthitanalytics.com/news/him-healthcare-big-data-analytics-skills-increase-in-demand

The first step in applying analytics to healthcare data is to create a centralized repository of all the available information extracted from EHRs, health information exchanges, claims data, etc. The main motive remains in interpreting the data to derive valuable insights, which will not only benefit health management, but also fuel business growth. This methodology is also being used to tackle mental healthcare data. Analytics is being used on community data, available in a centralized manner, to identify patients, with more vulnerability to certain mental illnesses. However, since complete information of patients is not always available in such centralized data platforms, inconsistencies in analyses remain. Read more at: http://healthitanalytics.com/news/mental-healthcare-big-data-warehouse-illuminates-care-trends

The first step in effective implementation of a health management initiative is stratifying patients according to the costs incurred by them, in order to properly target the healthcare programmes. It has been observed that the majority of healthcare costs are concentrated in a small fraction of the total patient population. Focusing on the healthcare of this fraction of patients, can greatly reduce costs for the entire population, while providing the appropriate care, valued by the patients and their families. Risk analytics incorporates claims and EHR data into risk scores, in order for health care providers to target their services efficiently. Read more at: http://healthitanalytics.com/news/population-health-management-starts-with-flagging-high-costs

Several sophisticated data techniques have been used to determine prospective services and specialties preferred at a particular geographic location. For example, psychographic data, provided by a customer analytics company, reflected local healthcare consumption trends. Demographic and marketing data, from health-planning agencies and hospital associations, as well as electronic health records (EHR) should be studied to visualize new market opportunities, i.e. new locations for health-care facilities. Healthcare strategists like retailers, are using geo-analytics—visualization tools incorporating sophisticated geographical and marketing data—to determine prospective health-care facility locations. Healthcare organizations should be analyzing data extracted from EHRs, along with health insurance claims data to understand the services used by potential healthcare customers.
Read more at: http://www.modernhealthcare.com/article/20150530/MAGAZINE/305309978/no-more-guessing-health-systems-tap-sophisticated-data-tools-to-pick

Sigmaway consultants have worked with clients in benefits solutions workspace providing analytics on healthcare plans. For more details visit http://www.sigmaway.us/

Big data is the new buzzword. It has become one of the imperative equipment required to conquer growth across industries. Here is how you can master the art by following thee 5 rules, coined by Rishi Sikka, Advocate Healthcare.

#1 Clue lies in the past: to be able to predict future, it is necessary to dig deeper into the past data and EHRs.

#2 Diversify risk through strategic distribution of resources.

#3 Impactability- healthcare organisations should focus where and to which extent do they impact.

#4 Take population Health as a predictive exercise, not academic.

#5 Minimize costs, work to provide best healthcare.

Read more at: http://www.healthcareitnews.com/news/5-tips-becoming-big-data-superhero

Big data is set to change the healthcare landscape offering better patient care, minimized hospital re-admission, and reduced costs but every new tool has a learning curve and so does this one in the healthcare industry. Big data in healthcare is referred to as Electronic Health records (EHRs), though relatively easy to collect but volume does not mean insight, it is necessary to extract data relevant to the heart of the specific problem. Apart from the 3Vs in big data – Volume, Variety, velocity, healthcare analytics also has another V, Veracity i.e. truthfulness or credibility, since it involves life and death decisions. But it is still a goal and not reality since the quality of healthcare data is highly variable. Another challenge is that the methodology requires technology and professional expertise for predictive analysis to fully utilise the data mining results to improve patient care. Read more at: https://www.healthcatalyst.com/3-reasons-why-comparative-analytics-predictive-analytics-and-nlp-wont-solve-healthcares-problems/

The big data has penetrated into healthcare. But merely collecting the patient records and keeping them in the form of electronic health records (EHR) is not enough. It starts with using programming software languages like R to sort through the huge database. R is an open source programing software used for statistical computation and data mining. In this case, R can be used to interpret the EHR for different purposes like side effects associate with a particular drug among a typical set of patients with similar records and could also help identify what causes the side effect. R can be customized according to the needs of the patient and the physician and thus can be helpful in providing personalized healthcare .Also, since R is open source software, it involves no cost and a large amount of documentation is available to learn the language. Read more at:http://www.healthcareitnews.com/blog/radical-potential-open-source-programming-healthcare