Additional data analysis using openIMIS data

Disease surveillance

Tracking of infectious diseases/ Disease outbreak investigations

• Demographics on individuals are useful for picking out disease patterns

• Claims data including diagnosis is relevant and an insurer actively scrutinizing (does diagnosis makes sense looking across trends, etc.), leads to the claim data having better quality diagnosis reported

• Any new data sources from health facilities (especially primary level in LMICs) that is of good quality/is reliable is very valuable

• If claims payment mechanism requires facilities to report claims regularly (daily or even monthly), it could be a source of real time information

• The system could provide flags to disease surveillance teams to track abnormal patterns.

• Quality of diagnosis made might be low (not an insurance specific problem but low quality professionals in health system) or might be different across facility levels or locations

• Provider payment mechanism might also influence reporting behaviour (eg. fixed payments monthly reimbursement based on head count might lead to lower emphasis on reporting correct diagnosis)

• Tracking is not appropriate for highly infectious "Notifiable Diseases" (for eg. Ebola outbreaks) which require dedicated structures and systems in place

• openIMIS can only flag potential outbreaks (eg. Influenza outbreak) and dedicated investigations teams are still needed to undertake further investigations and tracking through their own dedicated systems and structures

• Response needs to be quick to infectious disease outbreaks and hence flags from an insurance system might not support quick information depending on design of the insurance scheme (eg. claims submissions are delayed).

• Establish link between multiple visits/claims for the same illness episode by the same person (OSD-29)

• Tracking of referral cases - links between claims coming from different level of facilities of an individual for the same illness episode as part of referral chain (OSD - 58)

• Development of algorithms/logic to track standard patterns in data and subsequently link to disease surveillance system (an actor who is not a direct stakeholder of the system but part of dedicated disease surveillance team in country)

Diseases tracking

• Demographics on individuals are useful for picking out disease patterns

• Claims data including diagnosis is relevant and an insurer actively scrutinizing (does diagnosis makes sense looking across trends, etc.), leads to the claim data having better quality diagnosis reported

• System has both longitudinal and cross sectional data on diseases diagnosed and hence useful for tracking disease patterns (differences over time and across locations)

• Following the One Health approach in context of surveillance one could explore the linkage of health data with data on animal heath side.

• Quality of diagnosis made might be low (not an insurance specific problem but low quality professionals in health system) or might be different across facility levels or locations

• Provider payment mechanism might also influence reporting behaviour (eg. fixed payments monthly reimbursement based on head count might lead to lower emphasis on reporting correct diagnosis)

• openIMIS can only be source for this data for cross verification/triangulation for disease data reported in national health information systems

• Establish link between multiple visits/claims for the same illness episode by the same person (OSD-29)

• Tracking of referral cases - links between claims coming from different level of facilities of an individual for the same illness episode as part of referral chain (OSD - 58)

• Link to national health information systems used for tracking disease patterns

• Capturing location of where family lives and where claims are reported from to allow visualization of data on maps

Prescribing behaviour

Tackling Anti Microbial Resistance

• Diagnosis vis-a-vis type of drugs and quantity of drugs prescribed could help flag high use of antibiotics

• Could help track which line of antibiotics are being used and flag it for follow up action

• Provider payment mechanism might also influence reporting behaviour (eg. fixed payments monthly reimbursement based on head count might lead to lower emphasis on reporting correct diagnosis)

• openIMIS can only act as a flag and further investigations, verification, tracking and improvement measures are to be undertaken by other actors

• Link to national health information systems used in a country by the dedicated team/structure tracking anti microbial resistance

Tracking Neonatal treatments

• Claims data could offer tracking antibiotic therapy given for neonatal serious infections (sepsis, pneumonia, severe bacterial infection) in terms of types of antibiotics/regimes, duration of treatment and see how it differs across levels of health facilities and across locations.

Monitoring quality of prescriber

• Diagnosis against treatment administered could provide insights on whether standard treatment guidelines are followed in everyday practice

• Such monitoring and assessment of adherence to treatment guidelines requires a lot of investment and other limitations like biases, short observational period, etc. (observing practitioners, surveys, etc.), and perhaps in context of the prescriber submitting data to get payment, the data could act as an alternate source for monitoring quality of prescribers.

• Provider payment mechanism might also influence reporting behaviour (eg. fixed payments monthly reimbursement based on head count might lead to lower emphasis on reporting correct diagnosis)

• Capture data also on health care professional administering the treatment

• Capture additional details during claims submission like results from tests, etc.

Disease specific programme planning

Vaccine preventable diseases tracking

• Data could be linked to vaccination coverage dataset to indicate outcome of efforts made by vaccination programme

• Quality of diagnosis made might be low (not an insurance specific problem but low quality professionals in health system) or might be different across facility levels or locations

• Tracking such coverage efforts have dedicated systems and hence the insurance system can only be seen as a secondary system to provide information for further cross verification/triangulation with national information system dedicated towards tracking such programmes

• Capturing additional details of vaccines (serial number, etc.) provided and making links to information systems tracking vaccine distribution

Planing of preventive health programmes

• Disease profile pointing to communicable diseases or chronic conditions for eg. could lead to better planning of preventive services or better targeted health promotion programmes

• Quality of diagnosis made might be low (not an insurance specific problem but low quality professionals in health system) or might be different across facility levels or locations

Nutrition programme monitoring

• Main diagnosis from the ICD list could flag prevalence

Drug monitoring programmes

• Drug stock outs could be flagged if drugs were not dispensed to a patient even though the dispenser was expected to have these drugs

• Heavily dependent on data entered by dispensar whose behaviour might vary due to payment mechanism or other reasons leading to different data entered than what was actually dispensed

• Capture additional details of the drugs provided (eg. serial number)

• Link to information system tracking drug supply management

Road Accident

• Road accidents are a major source of deaths and health care costs in some contexts and programmes to monitor road accident outcomes lack data sources, which insurance claims data might be able to contribute to

Contributing to CRVS systems

• Insurance systems having regular opportunities to capture data from households (during renewals) provides a valuable opportunity to update data of an individual which could contribute to CRVS systems

• Claims data including information on deaths could be valuable data to pass on to CRVS systems

• Capture treatment outcomes like death or outcome of a delivery and explore additional datasets that might be common with CRVS or death registers

• Link to birth register as well based on outcome of a delivery

Poverty tracking programmes

• Data captured on individuals and families could expand and capture information on family status or income for eg.

• Possibility to easily add more data fields (during enrolment/renewals) to capture information on individuals and at level of family

HIV adherence

• Claims data could be used by programmes tracking HIV adherence of patients and can check claims data to see adherence over time, across locations, etc.

Pricing of drugs

• Heavily dependent on data entered by dispensar whose behaviour might vary due to payment mechanism or other reasons leading to different data entered than what was actually dispensed

Post marketing surveillance

• Drug approval agencies would be interested in monitoring impact of drugs post their approval and introduction into the market by observing drug effectiveness in patients and lack easily available data sources to be able to do this.

Pricing of health insurance benefit packages

Disease based costing

Population exposure to healthcare costs

Reinsurance pricing

Measuring outcomes

• As there is a possibility to capture feedback after the treatment (as part of regular feedback process or renewals process) is completed perhaps additional data could be captured (eg. PROMS style questions ) to be able to asses some established (patient reported) outcomes measures.

Resources allocation

Health inequities