Experience + EHR integration
Link NPS, CAHPS, grievances and feedback with encounters, scheduling, utilization and clinical data.
Healthcare analytics · Patient experience intelligence
Beyond satisfaction. Turn patient experience into actionable intelligence.
Connect what patients say with how they access and use care. Discover patterns in engagement, service delivery and resource utilization—and identify where improvement efforts may have the greatest impact.
What we can help organizations do
Connect the patient voice to operational outcomes.
Patient experience is more than a retrospective score. When linked responsibly with clinical and operational data, it can inform strategy, quality improvement and better allocation of care resources.
Patient segmentation
Explore differences among promoters, passives and detractors—and across service lines and patient populations.
Utilization intelligence
Study associations with emergency care, missed appointments, preventive services and continuity of care.
Text analytics & NLP
Turn open-ended comments into interpretable themes, concerns and opportunities for service improvement.
Predictive opportunities
Develop and validate models that use patient-experience and engagement signals to prioritize follow-up.
Measurement & governance
Assess survey response bias, subgroup representation, operational outcomes and the effectiveness of interventions.
Featured healthcare research
Beyond Claims: Using Patient Experience Analytics to Identify Potential Healthcare Waste
Health-system case study · NERDS-5 supporting researchA large academic health-system analysis linked patients' most recent Net Promoter Score (NPS) with electronic health record utilization data. The study explored whether experience segments were associated with distinct patterns of healthcare use.
~98,000Patients with NPS responses linked to EHR data
~489,000Patients seen across the study period
~20%Approximate share with NPS data; survey representativeness matters
Three signals that connect experience with utilization
Rates are per patient-day. Gold identifies detractors. Displayed figures are rounded values reported in the source study.
Emergency department utilization
Visits per patient-day
Detractors had higher observed ED utilization than both other groups (p < 10−8 for both comparisons).
Missed appointments
Missed appointments per patient-day
Detractors exceeded promoters by a small but statistically significant difference (p = 1.6 × 10−4).
Preventive services
Services per patient-day
Promoters and passives showed higher preventive-service utilization than detractors (p < 10−5).
How to interpret these findings: These are observed associations, not evidence that poor experience caused greater utilization or that improving NPS would automatically reduce costs. The study identifies promising patterns for further evaluation, not proven avoidable utilization or realized savings.
From insight to action
Three operational opportunities worth investigating.
Reactive care: Higher ED utilization among detractors suggests a useful starting point for investigating access, continuity and potentially avoidable acute-care pathways.
Capacity: Missed-appointment patterns can guide investigation of scheduling friction, outreach and unused appointment capacity.
Prevention: Differences in preventive-service engagement can help prioritize deeper analysis of gaps in routine care.
The strategic shift
Move from reporting patient experience as a standalone satisfaction metric to using it—alongside clinical, utilization and operational data—as an additional signal for targeted improvement.
Our practical approach
Five steps from patient feedback to measurable improvement.
Integrate
Connect NPS, CAHPS and feedback to EHR, encounter, claims and scheduling data.
Measure
Quantify associations with ED use, missed visits, prevention and continuity.
Correct bias
Test who responds, who is missing and how representation affects findings.
Predict
Explore interpretable models and prospective targeting, with proper validation.
Intervene & learn
Evaluate outreach and process changes against observed outcomes.
Responsible patient-experience analytics
Survey responses are a sample—not the entire patient population.
In the case study, respondents differed significantly from the overall patient population by gender, race and age. Those differences matter: apparent experience patterns may reflect who answers surveys, not only differences in care.
Our analytical approach includes representation checks, stratification, weighting where appropriate, sensitivity testing and transparent communication of uncertainty.
Evidence with boundaries
The source work was an academic health-system case study prepared as supporting material for a CMS NERDS-5 application. It was not a state Medicaid implementation or a demonstration of quantified Medicaid savings. The broader methodology is adaptable, but results should not be assumed to transfer unchanged to other populations.
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