Health In an implementation of openIMIS used to manage health financing mechanisms (such as insurance systems) capture a wealth of data on individuals, their families, their treatment, their health care costs, diagnosis patterns, medication prescribing behavior, cost of service provision, etc . Such systems take a is captured. This puts openIMIS unique position in the landscape of IT solutions dedicated to supporting various functions within a health care system. With openIMIS we would like to explore the use of such data from various perspectives to explore ways in which this data could contribute to strengthening health care systems. We encourage you to contribute to this discussion by providing us your ideas and inputs. You can do so by adding to the table below. You can also share (by uploading on this page below the table) relevant documentation on this topic like publications, consultation summaries, etc. and adding a summary of your documentation to the table in form of a use case in the table below, derived from the shared document.
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Tracking of infectious diseases/ Disease outbreak investigations
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- 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.
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- 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).
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- 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)
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- 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.
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- 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
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- 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
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- 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
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- 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
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- Link to national health information systems used in a country by the dedicated team/structure tracking anti microbial resistance
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- 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.
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- 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.
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- 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)
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- Capture data also on health care professional administering the treatment
- Capture additional details during claims submission like results from tests, etc.
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- 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
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- 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
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- Capturing additional details of vaccines (serial number, etc.) provided and making links to information systems tracking vaccine distribution
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- 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
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- 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
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- Main diagnosis from the ICD list could flag prevalence
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- Drug stock outs could be flagged if drugs were not dispensed to a patient even though the dispenser was expected to have these drugs
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- 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
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- Capture additional details of the drugs provided (eg. serial number)
- Link to information system tracking drug supply management
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- 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
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- 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
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- 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
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- Data captured on individuals and families could expand and capture information on family status or income for eg.
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- Possibility to easily add more data fields (during enrolment/renewals) to capture information on individuals and at level of family
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- 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.
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- 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
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- 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.
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- 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.
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Other resources demonstrating data analytics use cases from health insurance data:
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Currently, openIMIS provides two ways to analyze this wealth of data - through standard Reports and through DHIS2-based dashboards. As every implementation is unique in terms of data collected and the analysis needs, both methods require initial configuration/customization to ensure that the indicators available in the reports and dashboards are of significance to the specific implementation. The default indicators, reports, and dashboards in openIMIS are presented and discussed below.
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