AI market in India: Healthcare · Page 5 14 January 2020 Healthcare in India & AI Key Objectives of...

AI market in India: Healthcare

Abhinav Kumar, EY

14 January 2020 Healthcare in India & AI

Healthcare in India – Present Human Resources in the Healthcare System


Health Landscape in India

► Key Challenges

• Poor density of Primary care

• Poor utilization of Primary health care centres,

• Poor Quality of health facilities - undermining

effectiveness

• Increase in cost of non-hospitalized treatment

and proliferation of Uncertified

Medical Practitioners (UMPs)

• Lack of Awareness of transformative potential

of technology in healthcare

► Key Performance Metrics for Healthcare

• Maternal and child Health

• Family Planning

• Communicable diseases,

• Non communicable diseases,

• Elderly care,

• Emergency care

• ENT& Ortho


There exists gaps vis-à-vis global benchmarks and World Health

Organization (WHO) recommended norms owing to large disparity in the

condition

of healthcare services across states, with the most populous states being

the laggards.

Health Landscape in India


Key Objectives of the intervention of analytics

► Maximize the value of data and enable evidence based decisions:

o Cost Optimization (logistics optimization, inventory optimization etc.)

o Creation of a fact based Policy Framework

o Optimized Planning and Budgeting

► Improved process efficiency and enhanced monitoring:

o Reduce costs through increased efficiency: accurate diagnosis, preventive care, fraud detection

o Improve Service Availability and Quality of Service

o Increase Resource Efficiency / Operational Efficiency

o Creation of a state level healthcare research framework

► Creation of performance and perception monitoring framework

o Open source intelligence and social media analytics

o Empower staff to analyze data quickly with minimal dependence on IT

► Capacity building on usage of data and analytics

► Collective Learning / Knowledge management framework for state

► Improve data quality while ensuring absolute security and confidentiality


Health Outcome Metrics Health Determinants and Correlation Metrics

Mortality Morbidity Healthcare (Access & Quality)

Health Behaviors Demographics & Social Environment

► Mortality –Leading causes of death

► Infant Mortality

► Injury related Mortality

► Motor Vehicle Mortality

► Suicides

► Homicides

► Obesity

► Low Birth Weight

► Hospital Utilization

► Cancer Rates

► Dengue Rates

► AIDS Rates

► Tuberculosis Rates

► Motor vehicle Injury Rates

► Insurance Coverage

► Doctor Patient Ratio

► Service Providers per 1 lac of population

► No of Female health workers

► Infrastructure Availability (Ambulance, Chemist Shops)

► No of births attended by skilled staff

► No of infants provided free vaccination

► No of medical colleges

► Tobacco Use/Smoking

► Physical Activity

► Nutrition

► Alcohol Use

► Seatbelt Use

► Unsafe Sex

► Immunizations

► Age

► Sex

► Income/Poverty Level

► Educational Attainment

► Employment Status

► Homelessness

► Marital Status

► Domestic Violence at Home

Physical Environment

► Air Quality

► Sanitation Quality

► Drinking Water Quality

► Housing Facilities

Transforming Data Elements into Public Health Indicators


Suggested solution Overview for Healthcare Analytics


Functional Architecture

Mortality Data

Morbidity Data

Clinical Data

Insurance Claims

Financial Data

Operational Data

Data Warehouse

Data Integration &

Quality

Advanced

Visualization

Rule based

analysis

Advanced

Analytics

Unstructured data

analysis

Sourc

es o

f D

ata

Pro

vid

e S

pecia

lized I

nfo

rmation c

apabili

ties

EY Services

ConsultingSupport &

Operations

Capacity

BuildingImplementation

Program

Management

Support &

Operations

Rapid Outbreak

Response System

Health Surveillance

System

Epidemiological

Intelligence System

Health Risk

Management &

Analysis

Service Coverage /

Facility Analysis

Population Health

analysis & prevention

ETLData

WarehouseData Quality

Data

Integration

Reporting /

Dashboards

Advanced

Analytics

Social Media

Analytics

Technology Components

Platform Features Security

Role

Based

Access

Metadata

DrivenScalable

High

Availability

Open

Standards


Logical Architecture

Operational Data

Mortality Data

Morbidity Data

Insurance Claims

Financial Data

Clinical Data

Data Extraction,

Transformation

and Loading

Data Staging

Unstructured Data

from mails,

documents or Social

Media

Web Crawler /

ETL

Unstructured Data

Filtered / Indexed

ETL

Data

Transformation

Data Quality

KPI Calculation

Advanced

Analytics

Unstructured

Analytics

Data Marts

Subject Oriented Data Warehouse

Data Archival /

Backup

Reports

Dashboards

Ad-Hoc

Analysis

Alerts

VISUALISATION

Web based

Access

Mobile Access

Offline Access

Portal

USERS ACCESS

Role Based

AccessMetadata Enabled

INFORMATION SECURITY

Logical Architecture


KPI Repository for Public Health S.NO Area Indicator Unit Formula Benchmark 1

1aImproved access to

Healthcare Services

OPD Services No in Lakhs

No of OPD for benchmark (1)No of OPD per 1,00,000 lakh population for benchmark (2) & (3)

(1) X% y-o-y or m-o-m increase over base data

(2) National Average (per 1,00,000 population)

(3) Leading state (per 1,00,000 population)

1b

IPD Services No in Lakhs

No of IPD for benchmark (1)No of OPD to IPD conversion per 1,00,000 lakh population for the total number of hospital beds in the public hospitals for benchmark (2) & (3)

(1) % y-o-y or m-o-m increase over base data(2) X% y-o-y or m-o-m increase over base(3) Bed Occupancy Rate

2a

Increased affordability of

services through Health

Insurance

Total value of Claimsettled

Rs Lakhs Claim Amount y-o-y and m-o-m increase over base data

2b

Total no. of claims settled

Number Number of Claims Settled y-o-y and m-o-m increase over base data

3a

Improved access to

Emergency Transportation

Services

Ambulances Number No. of Trips Per Day y-o-y and m-o-m increase over base data

3bNo. of calls received by ambulance/ day

3cNumber of calls not serviced per day y-o-y and m-o-m decrease over base data

3d Janani Express

Number No. of Trips Per Day y-o-y and m-o-m increase over base data

3eNo. of calls received by ambulance/ day

3f Number of calls not serviced per day


KPI Repository for Public Health S.NO. Area Indicator Unit Formula Benchmark 1

4a

Maternal and Child Health

Full ANC coverage rate

% % of women getting full ANC coverage as against the total no. registered mothers for ANC and delivery

1) X% y-o-y or m-o-m increase over base data2) Comparison with National Average and Leading state

4b

Maternal Mortality Rate (MMR)

Per Lakh Live Births

Maternal Mortality Rate 1) X% y-o-y or m-o-m decrease over base data2) Comparison with National Average and Leading state

4c

Full Immunization Coverage

Numbers Immunization Coverage as against the expected/estimated children population in the state


4d

Infant Mortality Rate (IMR)

Per 1000 live births

IMR (Infant Mortality Rate) 1) X% y-o-y or m-o-m decrease over base data2) Comparison with National Average and Leading state

4e

Availing full complement of JSSK services

Number number of pregnant women receiving full complement of JSSK services against the total pregnant population


5a Focus on population

stabilization

Birth Rates Number births per 1000 population 1) No. y-o-y or m-o-m decrease over base data2) Comparison with National Average and Leading state

5b

couple protection rate

% % of couple in reproductive age groups protected with terminal methods of sterilization


5c

% % of couples in reproductive age group protected


KPI Repository for Public Health S.NO Area Indicator Unit Formula Benchmark 1

6a

Improved Healthcare

Service Effectiveness

Average Length of stay (ALOS)

In days No of days spent in IPD per patient X% Reduction y-o-y or m-o-m over the base data

6b Return to emergency in 72hrs

Number No. of patients returning to the hospital within 72hrs

6c Return to OT in 24hrs Number No. of patients returning to OT within 24hrs

6d Death within 48hrs Number No. of patients dying within 48hrs of Cardio-pulmonary resuscitation

6e Referral from PHC to CHC or DHC

Number No. of patients referred

7a

Maternal and Child Health

Caesarean sections deliveries % % of CS as against total no. of deliveries in the state

1) X% y-o-y or m-o-m decrease over base data2) Comparison with National Average and Leading state

7b Promotion of Institutional Delivery

% % of Institutional deliveries as against total no. of deliveries in the state


7c Reduction in still birth rate % % of still born as against total no. of institutional deliveries in the state

1) X% y-o-y or m-o-m decrease over base data2) Comparison with National Average and Leading state

7d Provision of free diet to mothers

Number Number of facilities with catering services for provision of free dietary services

1) X% y-o-y or m-o-m increase over base data

7e Hospital stay of 48hrs after normal delivery

Number Number of mothers spending 48hrs in 1) X% y-o-y or m-o-m increase over base data

8a

Promotion of rational use of

drugs and diagnostics

Generic Medicines Prescription

% % of Medicines made available through in-house generic store/Total prescription

X% Reduction in stock outs y-o-y or m-o-m over the base data

8b % of generic prescription X% increase in generic prescriptions y-o-y and m-o-m

8c Availability of Generic Drug Stores in Hospitals

% % stock out in hospitals for generic drugs 1) X% y-o-y growth over base data


Solution Modules

S.No. Module Name Module Function

1. Data Integration

(Extraction

Transformation and

Loading)

Automate Data integration across multiple

programs like Diabetes, Mother and Child

Tracking System, Tuberculosis etc. Integrating

data from multiple various systems into a

single central repository.

2. Data Staging This is a schema in a database where raw

data extracted from different source system is

stored as-is so that it can be further processed

and integrated.

3. Data Quality Data quality checks the health of data,

performs data standardization based on

business rules, identify duplicate values (For

e.g. one patient enrolled in multiple health

schemes)


Solution ModulesS.No. Module Name Module Function

4. Data Visualization /

Business

Intelligence

The Business Intelligence solution would

provide the visual representation and KPI

based analysis on the integrated set of data

as required in different areas. Some of the

sample KPI areas are as stated below:

• Fertility

• Mortality

• Morbidity

• Migration

• Health care service coverage

• Services Quality

• Burden of diseases

5. Open Source

Intelligence

This module has capability of capturing

unstructured data (e.g. through web crawling)

and perform meaningful analysis using

unstructured data


Solution ModulesS.No. Module Name Module Function

6. Advanced

Analytics

This module provides advanced analytics

capabilities like Decision trees, correlations,

segmentation, neural networks etc. Some of the

sample analytics scenarios are as stated below:

Survival Analysis - To investigate the effect of exposure to an

event (like Kuwaiti oil well fire smoke) and subsequent health

events by comparing the post event hospitalization experiences

of various event exposure groups.

Cohort Studies - To investigate the impact of follow-up at a

secondary prevention clinic on 1-year mortality in stroke patients

on a cohort taken from the Registry of Canadian Stroke Network.

Cross sectional studies - to detect associations between diet

and serum cholesterol in cross-sectional population studies.

Forecasting & Prediction - To evaluate quantile regression

model performance for high cost patients, to answer how a small

percentage of a population accounts for a large percentage of

healthcare expenditures.

Prediction of disease outburst based on historical patterns and

other significant indicators


Curation and navigation platforms:

• Where to go for help?

• What to do Next?

• Disease Management Apps

01Personal health cloud (PHC) :

• Data hubs to capture and curate a digital bio-portrait of deep personal data,

• Longitudinal view of health parameters

02

Data fusion platforms-

• Aanalyse incoming data

• Monitor continually high-risk clinical signals.

• Recommend alternatives for treatment

03

• Supra-system:Facilitated by global alliances -combining data, users & marketplaces Deliver value to all stakeholders

04

Foundational Elements of technology for Public Healthcare


Integrated Care Model – A Systemic View


AI in Healthcare – Scope

MICROSOFT

Project Inner Eye employs machine learning to differentiate

between tumours and healthy anatomy using 3D radiological

images that assist medical experts in radiotherapy and

surgical planning, among other things.

Industry impact: produce 3D imaging that pinpoints the

precise location of tumours and enables more accurately

targeted radiotherapy.

Methodology of applying AI in Healthcare

Case in point : AI in Healthcare

• AI aided inhaler based medication adherencesolutions - monitors correctness of drug deliverytechnique,

• AI aided early warning system - uses specializedspirometer and advanced analytics to help patientsidentify triggers, symptoms, trends and otherpersonalized insights.

• AI aided lung imaging - uses AI and high-resolution CTScans or X-ray images to help visualize both structuraland functional parameters of the lungs.

AI based Solutions

https://www.microsoft.com/en-us/research/project/medical-image-analysis/



Niramai

Niramai has developed - Thermalytix, a computer aided

diagnostic engine (powered by Artificial Intelligence) which

uses AI/ML over thermography images to detect breast

cancers and enable a low cost, easy to use, portable

solution and requires minimal human supervision.

Industry impact: uses high resolution thermal sensing device

and a cloud hosted analytics solution for analysing the

thermal images for early lump detection and proved 27%

more accurate than Mammography (70% better than

traditional thermography).

Exhaustive testing across 12K patients.

Anganwadi workers to be involved in future to get images

from handheld devices and analysing images to detect early

onset of lumps

Bangalore safe city project: Ambulance route

optimization

Demand forecasting – Number of ambulance calls expected

(Time series, drivers perspective)

Supply forecasting – Route optimization to minimize time to

reach health care facility resulting into saving of life as well

as fuel cost



► Hospital error is one of the leading causes ofpatients’ death. Such errors can be addressedand prevented by Artificial Intelligence.

► In the healthcare industry, nearly 86% of themistakes are preventable. In the next 5 years,AI health market will grow by more than10 percent.

► AI delivering high value in Speciality Care -pharma, radiology, and pathology

► Medical Imaging Analysis – for early andimproved diagnosis for cancer, radiology

► Healthcare bots to schedule appointments withthe patient’s healthcare provider. They improvecustomer service by offering 24 x 7 support

► Drug Discovery - AI solutions are beingdeveloped to identify potential therapies from vastdatabases on existing medicines, which could beredesigned to target critical threats

https://www.himss.org/jhim/archive/volume-17-number-1-2003


Health Record Based Patient Lifecycle

► EMR Based Patient lifecycle Framework under four phases

► Research and scheduling appointment,

► Outpatient experience ,

► Inpatient experience and

► Post discharge experience

► Assesses Gaps in service provision (like pricing , medicine availability and delivery , diagnostic analysis etc.)


A standardized vocabulary for everything medical

• Building a standardized vocabulary to represent all the components of the Indian health system.

It includes a nationalized directory of all the doctors or hospitals in India

AI-based data fusion system:

• Use advanced Natural Language Processing (NLP) techniques to bring large swathes of unstructuredindividual health care data into the standardized vocabulary

• Generate pooled, anonymized and secure health care data from individual health records

• Sentiment Analysis of the Unstructured Data from medical records, claim history etc

Focus on improving survival rates

• Use optimisation techniques to develop algorithms which help in early diagnosis, decreaseaverage time between occurrence of symptoms and treatment from 6 hours currently to under 6minutes.

• Develop facilities to upload readings, patient records on a secure cloud.

• Ambulance Route Optimisation Algorithms can be developed to minimise the travel time.

123

Building a Healthcare Ecosystem

Starting steps


Leveraging Analytics for Public Healthcare

Technology Platforms: CRM, ERP, HMIS

Data Integration and Data Management

Decision Support Systems:

Dashboards, Self Service Reporting

Analytical Models

& Optimizations

Workflow

& Operations

Strategy

► Without access to unified datasets, the ability to derive analytical value from the data is severely constrained

► Interoperability between technologies is one of the major factors impacting the adoption of analytics

► Operationalization of business drivers into measurable indicators

► Data Integration with Big Data, Hadoop, IoT, Crowd Sourcing

► Data vulnerability to hacks and other data security risks

► Changing disease profiles and patterns of re-emergence of diseases

► Inadequate Infrastructure

► Inadequate Doctors /Clinical Staff

► Multi-Layer Public Healthcare System

► Low affordability levels for general masses

► Low awareness levels for alternative medicine


Healthcare Data Fusion Platform based Analytics

Population health analytics

► Analytics can be utilised for demography and vital statistics Analysis

► Age wise/ regional trends for diabetes and most frequently prescribed medications can be

traced.

1Claims

Prediction

► Probability of claims for a disease condition and the approximate claim size can be predicted.

► The claims analysis will also show which hospitals and doctors are most preferred.

2Clinical

Pathways

➢ Standardization of nomenclatures of diagnoses and procedures, disease protocols can be analysed.

3Research

► Good cohorts for research can be created by using demographic information and desired clinical parameters

► Treatment effectiveness can be assessed by studying vital statistics like blood sugar, insulin etc

4


Telemedicine – Emerging Innovation in Health-Care

► Connected virtual care platforms allow for monitoring of vitals and

transmission of information by any home care

provider to the treating consultant.

► Combining this information with EMR data - holistic view of the patient

status

► Enhancing Citizen Service Delivery by combining diagnosis, consultation and

medicine via video consultations / telephonic consultations

► Flagging red signals during these consultations

► Advising on treatment methodologies


Signposts to data-driven business models


Appendix – Suggested Implementation Methodology for Healthcare Analytics


Implementation Methodology Analytics Techniques

Outlier Analysis & Distribution Analysis

Statistical Imputations

Analyze whether the data integrated from multiple systems is free from biases. Extreme values and outliers hamper the public healthcare profile analysis for parameters such as Age, Demographics, Poverty levels, Income Levels, Disease Prevention Awareness, Education Levels, etc

Variables that are extreme, outliers, missing (non mandatory columns), Imputations provide a means of synthetically incorporating values to prevent information loss or biased analysis.

Variables such as fertility rates, mortality rates, morbidity rates, healthcare insurance coverage, etc. may be imputed

Transformation techniques help to align the data that may be prone to extremities and outliers, and fit it against distributions, remove extremity, and hence zero on the right variables in their transformation form for traveller insights.

Nature of Analysis

• DF Fits, Cooks D• DF Betas Plots• Box Plots, P-P Plots• R Student / Studentized R

• Interval Variables: Mean, Median, Mid- Range, Distribution, Tree, Tree Surrogate

• Class variables: Count (Mode), Default, Default constant value, Distribution

• Normal (log, square, square root, exponential)

• power transformations with multiple tests like KS, Anderson Darling etc. to check.

Tools used

Statistical Transformations

Correlation/Association Analysis

Analyze which are the factors impacting public healthcare trends. Categorize indicative factors into positive connotations and negative connotations based on the direction of their impact. At this level Cause and Effect relationship is not known

• Pearson Method • Cramers V, Testing for Mantel

Henzel Test • Spearman Correlation Statistics

Data Quality

• Data is free from duplicate values, missing values, extreme observations or outliers, analytics is run on cleansed & unbiased data, thus making the outcomes more inferential.

• Data standardization process helps in comparing across multiple variables having different units in analytical models.

• Data Cleansing, Deduplication, Standardization


Implementation MethodologyAnalytics Techniques

Segmentation Analysis & Profiling

Hypothesis Testing

Create groups which are heterogeneous from each other but homogeneous within. Helps to identify focussed strategy for a homogenous group of citizens, enabling similar healthcare services interventions

Multiple hypotheses can be tested for gauging if there is a statistical significance of common practices, business rules, gut feeling vs a more scientific, data driven decision process. • “Mortality rates changes in urban-rural distribution”• “Tuberculosis in females under 20 years decreases fertility”

A reasonable demand forecast for epidemic control medicines in a region at a particular time of the year, would enable means to optimize the supply chain such that the anticipated demand may be matched with the available supply (medicines, clinical staff, medical infrastructure, etc)

Forecasting

Nature of Analysis

• K Means Segmentation• Clustering Method• Decision Tree, etc.

• T Test (Multiple Samples both independent & Dependent)

• ANOVA (Parametric, Non parametric)

• Forecasting Techniques: ARMA, ARIMA, ADF Test, De Trending

• Automated model comparison

(least MAPE, average square

error, etc) indicates the champion

model for scoring

Tools used

Identify statistically significant factors, prioritize & rank them: • Do ethnicity, food habits, age group, type of living, exposure to

certain diseases impact the fertility in different regions?• What are the drivers of a particular disease outbreak?• Estimated number of exposures to a disease in a region?• What is the quantum of impact for each factor?• How the factors can be ranked in terms of prioritization?

Prioritization of Factors

& Impact Analysis

• Regression Analysis• Decision Tree• Neural Network

• Enable visualization of Key Performance Indicators in a simple, user friendly manner, supporting different access levels for users based on their information needs and business roles

• Integrate with analytics output – factor ranking/event probability• Complement with self service report features

Dashboards Visualization

• Open Source Products – D3C• COTS Products – Tableaus,

Qlikview, Cognos, SAP BO


Thank You

AI market in India: Healthcare · Page 5 14 January 2020 Healthcare in India & AI Key Objectives of...

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