Presentation begins with how we are in the midst of a healthcare data explosion but how data explosion in itself may not mean anything significant. It is just the beginning.
- Govt of India had set up an Expert committee in 2010 to develop EMR / EHR Standards for adoption / implementation in the country. The Ministry of Health and Family Welfare notified the EHR standards in February 2016, has published them online for comments by the 20th of May Ministry of health & family welfare has already requested to make adoption of EHR standards as prerequisite in RFP/RFQ for all health related ICT initiatives in the States/Uts NeHA(National eHealth Authority) is proposed to be set up to specify standards, regulate and promote IT initiatives in health domain and establish eHealth systems for enabling inter-operability of data across hospitals. A national consultation on setting up NeHA was held on April 2016.
This slide talks about the various possible systems if interoperability and common data exchange is achieved.
1. Proactive Interventions: Reactive: Use historical medical data to identify the existing conditions for patients (this is to identify the gaps in documentation) Standardization: Create a hierarchical chronic condition categories (HCCs) structure that prioritizes the relative risk of all health conditions Predictive: based on the existing conditions, predict the possible future conditions that the patients may develop using the HCC and get them realized using encounters Encounter: Generate a report that can enable and direct the right encounter for the right patient, in the right venue at the right time 2. Intelligent Patient Assessment: Build system that providers can use at the point of care System can give inputs to providers based on intelligent assessment of patients existing and possible future conditions (using HCCs) Provider can confirm the possible future conditions, identified by the system 3. Prescriptive Analytics: System to predict possible medications based on the existing and possible future conditions (conditions confirmed by the provider) The system would use predictive analytics, trained by medical records from past xx years 4. Business Intelligence: Identify various metrics for population based on geography using historical data from various intelligent systems Sample Metrics: Diabetic population in a particular region/gender Common medical conditions in a region % of population with cardiac disease Regions with malnutrition problem
Infographic representation of Slide 3
This slide talks about few of the positive outcomes of having such systems in place and how healthcare has to move from retrospective approach and take a prospective approach.
Presentation ends on the note that, data explosion is just the beginning, interoperable data combined with the power of predictive analytics can have numerous applications that lower the cost of healthcare and improves the overall quality of care.
Data is just the beginning - Role of Predictive analytics in improving healthcare effectiveness - Raghu Verabelli,
Data is just the beginning!
The role of predictive analytics in
Managing Partner, GGK Technologies
Healthcare data interoperability for greater good Data is just the beginning
Interoperable standards and a
common Healthcare data exchange
holds the key to unlocking the
potential of healthcare data
Major impediments to a truly interoperable healthcare
↔ Lack of sufficient health information technology (HIT)
standards that include precise data definitions
↔ Lack of sufficient infrastructure for aggregating data
for each patient over time and across providers, and
protocols for seamless communication
↔ Non Standardized and non user friendly EMR/EHRs
↔ Limited IT infrastructure at smaller healthcare facilities
↔ Low digital adoption across health facilities
↔ Co-operation of organizations & administrators to
freely share healthcare data
Healthcare data exchange : World of Possibilities
Proactive Interventions & early diagnosis by care providers - Identification of a set of individuals who are
more likely to catch certain diseases based on their past data points, risk scores and a good predictive model
Intelligent Patient Assessment - Consume data from a common data exchange and aid doctors at point of
care with complete history of the patient collected across multiple healthcare facilities
E-Prescribing - Seamlessly integrate health facilities with Pharmacies & Laboratories
Prescriptive Analytics - Assist doctors with the prescriptions based on patient history
Business Intelligence - Analyze patient healthcare information by region/geography
NLP Sentiment Analysis - Analyze the sentiment of patient documents so doctors have a view of which
patient records need to be looked at on priority
Digital Healthcare – A better world
• Population Health Management
Prediction of disease spread patterns in order to control epidemics & outbreaks
• Improved effectiveness of Medical outcomes
• Faster and better co-ordination between healthcare providers and professionals
• Greatly reduced medical & prescription errors
• Retrospective to Prospective approach
Lower cost Better quality
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