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.


Conclusion
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.
















2 comments:

  1. How feasible it is to extract healthcare specific data when there is lot of confidentiality concerns in and around HIPPA being in picture ?

    ReplyDelete
  2. As long as the healthcare data is extracted within the secure confines of the customers infrastructure and is within its premises one can work out ways to stay compliant.

    ReplyDelete