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INSIGHTS FROM STATE DATA September , 2019

INSIGHTS FROM STATE DATA September , 2019. In God we Trust, All others must bring data Edwards Deming. Data at NHA. PMJAY Beneficiary Identification (BIS). PMJAY Hospital Empanelment (HEM). PMJAY Transaction Management (TMS). 50 Cr ELIGIBLE BENEFICIARIES. API GW.

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INSIGHTS FROM STATE DATA September , 2019

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  1. INSIGHTS FROM STATE DATASeptember, 2019

  2. In God we Trust, All others must bring dataEdwards Deming

  3. Data at NHA PMJAY Beneficiary Identification (BIS) PMJAY Hospital Empanelment (HEM) PMJAY Transaction Management (TMS) 50 Cr ELIGIBLE BENEFICIARIES API GW State IT System for PMJAY PMJAY Data Warehouse CCDT, Greivance and other data systems PMJAY Fraud Analytics PMJAY Insights

  4. Insights Portal Created by NHA to share standardized reports to states for day to day management of PMJAY Over 40+ dashboards available covering various factors are available. Several State specific reports have also been published on request.

  5. Data driven decision making at NHA : Management Dashboards Near real time dashboards help track performance of a state or a district on various indicators for close monitoring • Performance Indicators can be tracked across multiple dimensions • State Performance

  6. M & E – A look at utilization under PMJAY Hospitalization Rate for richest 40% of population > 5% Hospitalization Rate for the bottom 40% of population < 2.5% Utilization is expected to be greater than 2% of covered population under PMJAY. 2% equals 32 admissions per lakh of population covered every week. NHA uses this metric to review utilization under the scheme across states Pre-auth/Lakh population

  7. WHAT FACTORS DRIVE BETTER UTLIZATION? Awareness – Do e-cards improve awareness? Availability of care – Does empaneling more hospitals help?

  8. % of Familes who have at least 1 e-card The following states / UTs carried out strong drives to issue e-cards just after launch • Chhattisgharh • J & K • Kerala • Himachal Pradesh • Uttarakhand • Jharkhand • Mizoram

  9. Higher e-card penetration = Higher awareness and utilization Pre-auth per Lakh Population National % Families with at least 1 E-card Gujarat Chhattisgarh Mizoram Kerala Himachal Pradesh Tripura Jammu And Kashmir Meghalaya Jharkhand National Avg. Pre-auth/lakh pop. Tamil Nadu Uttarakhand Karnataka Haryana Manipur Maharashtra Madhya Pradesh Uttar Pradesh Bihar Nagaland Punjab

  10. Improving e-card penetration Take Advantage of Auto Approval Technology driven by Data Utilize the new policy that opens up e-card issue via more partners Use data to identify areas that need more e-card issuals

  11. Auto Approval in BIS to handle higher volumes • Problem: Manual process of verifying and approving e-cards by ISA led to a huge pendency leading to delay in generation of e-cards • Solution:implementing an auto approval algorithm where machine mimics human verification process • Used analytical techniques to assign weights to critical variables for developing a match score between beneficiary (Aadhaar) data and SECC data

  12. Village level penetration in greenfield states % Villages Covered Distribution of Districts with % Villages covered with atleast 1 e-card Distribution of Districts with % Villages covered with atleast 1 treatment • All districts have been covered with at least 1 e-card and 1 treatment • 81% of the villages in major greenfield states have been covered with atleast 1 e-card and 43% with atleast 1 treatment States analyzed – Bihar, Chhattisgarh, Haryana, HP, Jharkhand, J&K, Manipur, Meghalaya, MP, Uttarakhand, UP 25-50% > 75% 50-75% < 25%

  13. Active Hospital more important than Empaneled Hospital • NHA defines Active Hospital as one that has raised at least 4 pre-auths in the last 4 weeks • Use data to identify inactive hospitals in every district • Identify reasons for why a Hospital that sought empanelment is inactive. • Hospitals Empaneled = 18,279 • % of Hospitals Active = 46% • % of Public Hospitals Active = 44% • % of Private Hospitals Active = 47%

  14. Village level penetration: Jharkhand # Active Hospitals % Villages Covered % Villages covered with at least 1 treatment E-cards Generated, hospital availability and treatment are inter-related % Villages covered with at least 1 card Hospitals across districts > 75% > 20 50-75% 10-20 25-50% 5-10 < 25% 1-5

  15. Village level penetration: Uttar Pradesh # Active Hospitals % Villages Covered % Villages covered with at least 1 treatment Even though BIS penetration is quite good but # treatment has limited success which can be correlated with hospital availability % Villages covered with at least 1 card Hospitals across districts > 75% > 20 50-75% 10-20 25-50% 5-10 < 25% 1-5

  16. Using data across states to identify outliers

  17. Identify packages with potential abuse – when utilization differs significantly than National averages.

  18. Reservation of packages and impact on utilization • Impact of reservation of packages on growth of scheme • A state had relatively lower growth and poor participation from private hospitals • Analysis of utilization and claims data showed that low admissions can be directly attributed to extensive reservation of packages for public sector • Illustrated potential for growth in coverage and scheme if certain packages were unreserved for private hospitals to offer services • Following the recommendations, state unreserved 157 of the 303 reserved packages and an increase in private sector participation was observed

  19. Utilization data used to design Health Benefit Packages 2.0 Discontinued packages Additional packages based on analysis of Unspecified Packages • Qualitative discussions were backed by utilization data to take decisions on discontinuing packages due to reasons like – • Under utilized • Older molecules Cognitive and quantitative analysis of unspecified packages led to introduction of new packages • Examples • Coiling - Pseudoaneurysms of Abdomen • Oesophageal Diverticula /Achalasia Cardia • Young's operation and Rhinosporidiosis Examples • Removal of K- Wire • Facture calcanium • ORIF both bone forearm

  20. Using data to help monitor performance of claim processing

  21. Proposed framework for comparing the different models IMPACT: Insurer/ISA Monitoring & Performance Analysis • Claim Rejection Ratio • No. of Queries Raised • No. of Grievances related to Claims • 1)Turn Around Time (TAT) • Pre- auth approval • Claim approval • Claim payment . • 2) Productivity • Pre-Auth per PPD • Claims per CPD • Claims per CEX 3) Approver Inactivity Performance Efficiency Proposed parameters to compare insurance, trust & hybrid model • Morbidity / Pre-auth Frequency • Loss Ratio • Burning Cost • Investigation and Outcomes • Audits and Outcomes Investigation & Audits Utilization Infrastructure • Human Resource Deployed at State • Human Resource Deployed at Districts • Quality of Human Resource Deployed • Other Infrastructure Support

  22. IMPACT Dashboard- Monitoring & Performance Analysis Rating and scoring has been drafted for evaluation of Insurer/TPA performance Efficiency Utilization Score card for TPA accounts factors like efficiency, utilization, Infrastructure, performance and Investigation/Audits

  23. Data and Fraud Control

  24. NHA is investing in strong fraud analytics • Conducted a 6 month proof of concept with 5 vendors • Each vendor setup their systems within NHA and demonstrated the capabilities of their models and teams to identify various types of fraud • NHA has currently released an RFP and will shortly onboard a Fraud Control partner. • The system will score transactions – from pre-auth and raise alerts (with reason) for claims that need investigation. • States require to setup strong State Anti Fraud Units (SAFU) to investigate and take forward these cases.

  25. Fraud: Social Network Analysis Ineligible members added to beneficiary family in Hospital A HOSP A These 5 females had a delivery within a week of enrolment in the same hospital as that of PMAM Hospital A PMAM of Hospital A Claims of added members are registered in the same hospital Alert from NAFU to SAFU • Investigation by SAFU • Impersonation Confirmed • PMAM Deactivated • All Incorrect Ecards cancelled • Controls implemented in IT • One Single PMAM adds 12 family members to a single family on the SAME DAY • 5 of the 12 Female members were added on the SAME DAY as Daughter in Law with Adoption certificate as Document Proof

  26. Democratizing Data Enabling States to setup their own data warehouse and analytics teams Working on an Open Data model policy and methods to make data available for researchers on request

  27. States are encouraged to invest in Data Analytics Teams Implementing a State Datawarehouse Advantages of SDWH Data Warehouse National data warehouse environment State data warehouse environment

  28. If we have data let’s look at dataif all we have are opinionslet’s go with minejimbarksdale

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