Frequently Asked Questions
1. How quickly can we see value from predictive analytics?
Our agile framework is designed to produce measurable outcomes fast. During the first two weeks we define success metrics, identify data sources, and establish secure connectivity. By weeks 3–6, our data engineers clean and normalize your EHR and claims feeds while data scientists prototype the first predictive model. Internal user-acceptance testing happens in weeks 7–8, and a limited production roll-out follows immediately after. Most Ohio healthcare clients see tangible impacts such as decreased readmissions or improved staffing accuracy within the first 90 days. Continuous optimization ensures your ROI keeps growing quarter after quarter.
2. Do your solutions integrate with Epic and Cerner?
Yes. Our integration strategy embraces the standards promoted by both vendors and the Office of the National Coordinator for Health IT (ONC). We leverage a combination of HL7 v2 messages for real-time ADT feeds, FHIR APIs for granular encounters and patient resources, and vendor-specific SDKs where appropriate. Our team has completed dozens of successful integrations with Epic (Clarity, Cogito, Hyperspace) and Cerner Millennium, meaning we understand the nuances of Chronicles data models, SmartLinks, and MPages. We handle the heavy lifting of mapping, validation, and ongoing maintenance so your internal IT team can stay focused on mission-critical tasks.
3. How do you ensure data security and HIPAA compliance?
Security is embedded in every layer of our architecture. Data in transit is safeguarded with TLS 1.2+ encryption, while data at rest is protected via AES-256. Role-based access controls are enforced through your identity provider (Okta, Azure AD, or on-prem LDAP) using SAML or OAuth 2.0. Each environment—development, staging, production—lives in its own isolated VPC or VNets with network ACLs and micro-segmentation. Our standard deployment is aligned to HITRUST CSF controls and undergoes quarterly penetration testing and annual SOC 2 Type II audits. Cabot also provides a Business Associate Agreement (BAA) to ensure full HIPAA coverage for our services.
4. Will our clinicians trust and adopt the predictive insights?
Clinician adoption hinges on transparency, relevance, and workflow fit. We involve physicians, nurses, and care coordinators from day one to co-define the clinical rules for each model and establish acceptable alert thresholds. Explanatory AI techniques—such as SHAP values and feature importance charts—are built into every dashboard so users can see the “why” behind a given risk score. Furthermore, predictions surface natively inside your existing Epic or Cerner interface: a risk flag in the patient storyboard, a SmartLink in discharge summaries, or a customizable Best Practice Advisory (BPA) alert. Ongoing feedback loops capture clinician sentiment and drive continuous refinements that bolster trust and sustained usage.
5. What ongoing support do you provide?
Our partnership does not end at go-live. You receive a dedicated Customer Success Manager, direct access to data scientists for model tuning, and a 24-hour SLA on critical support tickets. Weekly stand-ups review KPI dashboards, model-performance metrics, and user adoption statistics. Quarterly business reviews align the roadmap with your evolving strategic goals—be it expanding to new predictive use cases, integrating additional data sources such as social determinants, or preparing for upcoming CMS quality measures. All upgrades and security patches are automated via our DevSecOps pipeline, ensuring minimal downtime and zero impact on patient care.
6. Can the models adapt to new data or changing patient populations?
Absolutely. Predictive accuracy can drift when documentation practices, patient demographics, or care pathways evolve. To mitigate this, we incorporate automated drift detection that compares real-time data distributions to historical baselines and flags deviations. Scheduled retraining jobs—triggered monthly or upon hitting a drift threshold—use the latest labeled data to refresh model parameters. Before deployment, each retrained model passes through statistical validation and clinician review to maintain both accuracy and clinical relevance. This closed-loop process ensures you are always working with models that reflect the most current realities of your patient population.
