December 27, 2012

5 Disruptive Big Data Use cases in Health Care analytics to heal body & soul

Big data + Health care is an intersect we at Flutura are passionate about for 3 reasons

Reason-1 : It has large pools of  untapped data pools which are not “juiced” for patient care intelligence
Reason-2 : Healthcare is definitely ripe for disruption using advanced data analytic
Reason-3 : The outcomes of disruptive transformation touch people in a very human way

These beliefs got us really moving and nail the use cases which can move the patient experience needle. We have created an extensive catalog of Health care use cases which are still white space opportunities to improve care givers efficiency & heighten patient’s experience. As care givers start having access to wider and deeper data pools they have started putting this into a ‘data blender’ thereby stirring / moving the needle on  care giver efficiency metrics and patient experience metrics

In this blog we will share 5 sample use cases from our extensive use case catalogue designed to extract knowledge about patient care patterns from unstructured transcripts of doctors, nurses and diagnostic lab along with location data available from new instrumented devices

So what are 5 use cases which can help?

Use case-1: Keyword mining of doctor’s/Lab transcripts using text mining and co-relations to patient outcomes.

As a patient interacts with the hospital thru multiple touch points – nurse touch point, Doctors touch point, Diagnostic touch point etc. At each touch point encounter there is a lot of semi structured information regarding the patient’s conditions which is captured- for example a CAT scan report would have preliminary interpretation regarding the state of the nerves and blood flow conditions of the brain. This unstructured text from a doctor, diagnostic lab or nurse’s observations contains a rich gold mine of intelligence regarding the patient’s condition which can influence a patient’s outcome. These outcomes can be further divided into

However this rich source of information regarding a patient’s condition is also untapped. A text mining process can be used to harvest this intelligence into an Clinical Disease Repository (CDR ). 
- Coronary Keyword watch list
- Diabetic keyword watch list

The Clinical Disease Repository can contain early warning signals regarding co-relation frequency of occurrence of  specific words in the unstructured text and the clinical outcome

Use case-2: Location aware application analytics for enhancing customer experience and optimizing nurse/doctor deployment

A range of new solutions within hospitals have  RFID chips which are embedded to patient’s card or Doctors card or nurse which can relay the location information of the patient/doctor in real time. This location data is a real new data pool with huge implications for effectively managing patients experience and optimising resource within a hospital. For example we can create a simple vectors like nurse/patient ratio, nurse mobility index etc. We can also create models to see the strength of the relationships between patient satisfaction index and nurse/patient ratio. We can then define optimal nurse/patient ratios for different sections of the hospital – OPD/cardiology/paediatric wards for example may need higher nurse/patient ratios than say for example dental department. Once set anytime this goes crosses a threshold an alert can be send to the Head nurse to alleviate the risk of a under serviced patient. We can also use the nurse mobility index to decide how the various departments must be co-located within the hospital to  improve patient outcomes and optimize use of expensive health care equipment

Use case-3: Telemedicine Analytics

Telemedicine platforms can go to the patient when it is difficult for the patient to come to the hospital. A telemedicine platform can capture various vitals of the patient like temperature, Heart rate, Blood Pressure and ECG which can streamed to a central repository in real time via satellite. Once collated a series of triggers can be placed on the data to sense and respond to real world health conditions
ALERT-1: If growth in concentration of BP with statistical significance found for male in the age group 30-45 in a specific zip code say 08837 from chi square test then hold road shows to sensitize the inhabitants in the zip code on healthy eating habits
ALERT-2 : If the number of patient segment migrations> 10 %  based on actual diagnosis events moves from cluster-2 to cluster-5 then proactively import preventive medicine in bulk to cater to growing needs

Use case-4: Apriori sequence analysis to define new clinical pathways

Apriori algorithms can be used to unearth interesting sequences in data occurring close to each other before a clinical outcome. These could be time ordered sequence of events.This would help us create episode rules like “If ‘restlessness’ & ‘insomnia’   occurs in the transcripts there is a 60 % chance that a coronary episode is imminent” . These can trigger proactive interventions which can help reduce the chances of an adverse event or a hospital admission event.

Use case-5: Adverse events signal analysis sandbox

Adverse events are a reality of today’s health care environment. An adverse event can be defined depending on the context
-          Schizophrenic event
-          Angina event
-          Mortality event

Examples of signals are
 - Frequency of Low grade fever > 3 times  multiple times in last 90 days
 - Recency of last episode of memory failure ...

One can build a Disease signal repository which is an early warning system for the hospital consisting of the strongest predictors of a patient outcome.

Care providers can always learn from adverse events in a systematic way and to extract previously unknown signals which can be used to mitigate its recurrence.  These signals can be small pieces of evidence which when triangulated really amplifies the situation. These medical signals are buried in the avalanche of patient information and most times there is no bandwidth within the care givers organisation to extract the same and reduce the number of blinds spots regarding the adverse event.

To summarize we @ Flutura strongly believe Patient data can be the lifeblood of a care giver and must be treated like gold. Flutura strongly believes that Patient care intelligence platform can serve as a bridge between care providers good intentions and patient’s experience of human touch by harvesting new pattern signals to trigger personalized actions.

Bertrand Russell was right when he said “One must care about a world one cannot see”. Flutura is proud to advance these 5 Health care use cases which can be used to “see” a new world of “Health care signals” in the avalanche of patient care data thereby improving the human condition.

December 8, 2012


A data scientist at Flutura has to wear multiple hats in order to deliver next generation analytical solutions in the sectors we operate in namely energy, telecom, digital and health care industry. In order to do that he/she has to wear 3 hats

-         The BUSINESS  hat
-         The MATH hat
-         The DATA hat

Most of the time it’s easy to fathom the depth of the data scientists math / algorithmic knowledge and the depth of his/her understanding on handling high velocity data and unstructured data points. But one area of weakness is the business dimension. So how do you decide whether a data scientist can be put in front of the business? This blog talks about 8 different tests Flutura executes to decode the business acumen of a data scientist


Human Beings are wired more to listen to stories than to read numbers. Flutura data scientists were doing data forensics on mobile app funnel drop analysis for an online travel agency was able distil the quintessential essence of all essences - That the mobile user who was getting dropped was a 20 something, last minute booker travelling between metros and trying to complete the transaction from a Samsung mobile using Android os and the friction point was the payment gateway
-         Can the data scientist translate numbers into stories? This is a very important tool to build bridges with business. Else a data scientist has the risk of getting struck in the world of math and unable to make the connect.


It’s very important for a data scientist to triangulate from key insights. A Flutura data scientist working on Telecom security use case was able to connect the dots when he was able to see a co-relation between multiple failed login attempts + successful patch download event and a surge in network traffic which was a result of the security hole in the patch which was downloaded.
-         Can the data scientist connect the dots and form a “necklace” from the pearls of insights discovered from cryptic log file data points?


One of the biggest risks in a big data project is using data to solve the right problem. There are many use cases a data scientist can curate … How do we identify the use cases which are $ denting from the use cases which have marginal impact?.Big data use cases can be segmented into 2 categories … those which move the needle incrementally vs those which disrupt. Its very important to keep this distinction in mind. Flutura was able to shepherd an ecommerce company into introducing new payment products after most of the transactions were dropped at payment gateway. This minor tweak resulted in the friction point being removed and a huge upswing in revenues
-         Can the data scientist tease out business themes where a use case can unlock disproportionate revenue making potential for the organisation?
-         How would a data scientist go about teasing out the business themes to move the needle?
-         Which are the best “impact zones” in a business process which are “ripe” for big data?


Let’s face it – data driven domain knowledge can reduce the learning curve required to understand domain and is deeper than armchair based experiential knowledge. Multiple engagements Flutura has executed has proven to us that a data scientist can glean far more knowledge about the nuances of a business by doing getting his/her hands dirty on exploratory data analysis(EDA), and eyeballing univariate and bi-variate results.
-         Can the data scientist “sniff the domain out” by examining EDA outputs and getting the business to put the numbers in context?


Most of engagements , the end result is a  suave looking ppt with lots of eye candy graphs which result in a feel good effect but business is left wondering on the actions that can be driven out of the exercise. In Flutura our mantra has been “Actions not insights”. One of the use cases we executed resulted in high value customers who are vulnerable to churn away being redirected in real time to high touch contact centre agents who would call them instantly and offer an instant rebate to woo them back
-         What was the data scientist’s role in operationalizing actions or did his prior engagements end with recommendations? There is a big difference between the two


Carving out new use cases and possibilities from new data pool is both an art and a science. A Flutura data scientist was able to use search logs which were typically discarded to decode the travel intent of an online booker – is it a price sensitive traveller or  a value conscious traveller ? is the traveller an early bird or a last minute booker. This use case to create behavorial tags from search logs resulted in more intelligent outbound actions
-         Can we give a raw data set and can the data scientist take 3-5 minutes to curate an interesting possibility from the raw data set ?
-         Where would he or she start in the big data ocean and zero in on the right ‘catchment’ of use cases


Every big data voyage requires a north pole in terms of measuring success for the engagement. A data scientist must be extremely clear or what constitutes success for the business stakeholders be it a sandbox setup or a full fledged production setup of a Hadoop cluster.
-         Can the data scientist work with business to articulate the ‘as is’ state and the expected ‘to be’ state of the decision making process after the analytical solution is implemented?

Test-8 : THE “WHAT DO YOU SEE” test

The ability to take an analytical output and translate them into a series of English statements – this constitutes Flutura’s “What do you see” test. The sample analytical outputs can be
-         Key word frequencies from text mining
-         Scatter plots
-         Box plots measuring behavioural volatility of customer balances
-         Bi-variate cross tab outputs
-         Clusters from a segmentation output etc
-         Can the data scientist construct 3-4 meaningful English statements from the above sample analytical outputs?
 If so he/she would have crossed the big chasm from math to a business pattern which can be perceived by business

So in a nutshell here are 8 questions to ask
o   Can the data scientist narrate a compelling and resonant story from the data patterns?
o   Can the data scientist connect the dots and form a “necklace” from the pearls of insights discovered from cryptic log file data points?
o   Which are the best “impact zones” for use cases which are “ripe” for big data?
o   Can the data scientist “sniff the domain out” by examining analytical outputs and getting the business to put the numbers in context?
o   What was the data scientist’s role in operationalizing actions or did his prior engagements end with recommendations?
o   Can we give a raw data set and can the data scientist take 3-5 minutes to curate an interesting possibility from the raw data set ?
o   Can the data scientist work with business to articulate the ‘as is’ state and the expected ‘to be’ state of the decision making process after the analytical solution is implemented?
-         THE “WHAT DO YOU SEE” test
o   Can the data scientist construct 3-4 meaningful English statements from clustering outputs, keyword frequencies, Box plots and other analytical outputs?

These tests are by no way collectively exhaustive or perfect. But it serves as a reasonable starting point to get the right DNA of Data Scientists into the organisation. Else we run the risk of having people who just knows how to create a Hadoop cluster :) as being labelled a data scientist.
As the saying goes “The real voyage of discovery consists not in seeking new landscapes but in having new eyes.”- Marcel Proust
Good luck with your efforts to recruit the rare species – the holistic data scientist :) !!!