Intacare supports the delivery of demonstrable, value-based healthcare.
To enable payers (government, insurers etc) to more effectively commission healthcare services on behalf of their populations it is imperative that clinical service providers can demonstrate outcomes-based clinical value if they are to receive referral volumes and appropriate reimbursement.
Intacare’s Precision Insight Engine (PIE) provides targeted reports on individual or cohort patient experience, toxicity and outcomes, as well as service utilisation, prescription patterns and protocol adherence.
By highlighting long-term outcomes risk and presenting evidence-based future outcomes risk management plans the service provider can demonstrate that they are focussed on the delivery of sustainable quality and affordable health care – the true essence of value-based healthcare.
Intacare’s insight reports also provide vital quality and value transparency to payers to support service commissioning decisions.
PROs (patient-reported outcomes) questionnaires are vital tools in ascertaining clinical outcome and future risk, and need to be assessed in combination with personal circumstances, the patient’s treatment pathway and previous PROs if the root cause of clinical outcome improvement or decline is to be fully understood and managed.
Intacare’s Precision Insight Engine (PIE) takes PROs reporting to a new dimension.
Intacare uses advanced algorithms developed by our academic teams and employs AI (Artificial Intelligence) to analyse PROMs and provide real-time analysis and insights for clinicians, enabling more timely and person-centric care to be delivered, and improved outcomes to be achieved.
PIE consumes a variety of holistic and tumour-specific PROs, analyses these data, and applies advanced academic logic and AI to provide insightful PROs reports that highlight:
- Severity: Individual item and symptom severity measures
- Trends: Tracking and highlighting positive or negative movements in symptoms, domain or total scores
- Symptoms clusters: Identifying clusters of low scoring symptoms that are evidenced to indicate patient has unmet needs or risk of poor outcome
- Severity breaches: Identifying symptoms that beach pre-defined or tailored severity levels
- Critical symptoms: Highlighting specific symptoms, that evidence to the individual’s disease might be progressing or recurring
- Critical incident risk: Highlights when symptom(s) present that indicate the patient may be having or is at risk of having an acute medical episode eg. neutropoenia
- Root cause: PIE uses advanced machine learning to correlate PROs with other clinical data to assess rout cause of outcome improvement or decline.