List of eClinicalWorks healow No-Show Prediction AI Model Customers
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Since 2010, our global team of researchers has been studying eClinicalWorks healow No-Show Prediction AI Model customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased eClinicalWorks healow No-Show Prediction AI Model for Predictive Analytics from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using eClinicalWorks healow No-Show Prediction AI Model for Predictive Analytics include: Urban Health Plan, a United States based Healthcare organisation with 865 employees and revenues of $141.0 million, Total Health Care - Baltimore City, MD, a United States based Leisure and Hospitality organisation with 350 employees and revenues of $50.0 million, Centerpoint Health, a United States based Healthcare organisation with 130 employees and revenues of $47.0 million, HealthCare Choices NY United States, a United States based Healthcare organisation with 100 employees and revenues of $5.0 million and many others.
Contact us if you need a completed and verified list of companies using eClinicalWorks healow No-Show Prediction AI Model, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Centerpoint Health | Healthcare | 130 | $47M | United States | eClinicalWorks | eClinicalWorks healow No-Show Prediction AI Model | Predictive Analytics | 2024 | n/a |
In 2024, Centerpoint Health deployed the eClinicalWorks healow No-Show Prediction AI Model as a Predictive Analytics application to improve scheduling and patient access across its federally qualified health center network. The deployment is positioned to identify appointments at high risk of no-show and support targeted outreach workflows, with the organization reporting 90 percent model accuracy and a roughly 24 percent increase in show rate for appointments flagged high risk.
The eClinicalWorks healow No-Show Prediction AI Model ingests historical appointment and attendance data from the eClinicalWorks EHR and surfaces a no-show prediction percentage directly in the eClinicalWorks schedule. Configuration work focused on embedding the prediction score into appointment views, enabling staff-facing prompts for reminders and rescheduling, and recording patient-reported reasons for missed or canceled visits within the EHR.
Operational coverage includes primary care, dental, obstetrics and gynecology, and behavioral health services across Centerpoint Health sites serving Butler, Warren, and Hamilton counties in Ohio. The implementation impacts scheduling, patient access, front-desk and outreach team workflows, and care coordination by shifting certain outreach decisions from manual triage to model-informed prioritization.
Governance and process changes established by Centerpoint Health center on a feedback loop with the vendor to refine prediction behavior and on documenting reasons for missed appointments in the EHR to improve model inputs. Reported outcomes from the deployment are limited to the stated accuracy and show-rate improvement, and the implementation narrative centers on embedding predictive scores into daily scheduling and outreach operations rather than wholesale platform replacement.
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HealthCare Choices NY United States | Healthcare | 100 | $5M | United States | eClinicalWorks | eClinicalWorks healow No-Show Prediction AI Model | Predictive Analytics | 2024 | n/a |
In 2024, HealthCare Choices NY integrated the eClinicalWorks healow No-Show Prediction AI Model with its eClinicalWorks EHR to predict likely no-shows and enable targeted patient outreach for scheduling and access. The implementation leverages Predictive Analytics to identify appointment-level no-show risk within clinical operations and patient access workflows at the community health center in New York.
The deployment embedded the eClinicalWorks healow No-Show Prediction AI Model into scheduling processes and outreach routines, operationalizing a risk score to prioritize outreach for high-risk appointments. Configuration focused on using appointment and patient contact signals to drive automated or staff-triggered outreach, aligning model outputs with existing scheduling queues and patient communication channels.
Integration surface areas included the eClinicalWorks EHR scheduling module and patient contact records, enabling the patient access team to act on model predictions without separate tooling. Operational coverage centered on clinical operations and patient access functions, with rollout prioritized to appointment segments flagged as high risk to concentrate resources where the model indicated greatest likelihood of no-shows.
The community health center reported that after using the eClinicalWorks healow No-Show Prediction AI Model the show rate for high-risk appointments increased from 10.4% to 26.5%, a 155% improvement, demonstrating a measurable change in appointment adherence and access to care.
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Total Health Care - Baltimore City, MD | Leisure and Hospitality | 350 | $50M | United States | eClinicalWorks | eClinicalWorks healow No-Show Prediction AI Model | Predictive Analytics | 2023 | n/a |
In 2023, Total Health Care Baltimore City MD implemented the eClinicalWorks healow No-Show Prediction AI Model, a Predictive Analytics application to target outreach and reduce missed appointments within scheduling and patient access functions. The deployment was focused on the FQHC clinical operations and patient access teams in Baltimore and aligned predictive scoring directly with appointment workflows.
The implementation configured the healow No-Show Prediction AI Model to generate appointment level risk scores, flag high-risk patients, and drive automated outreach triggers and reminder sequencing integrated into operational scheduling. Configuration emphasized risk stratification, outreach prioritization rules, and near real time surfacing of predictions to patient access staff.
The solution operated alongside eClinicalWorks, with the healow model consuming scheduling and patient record inputs from eClinicalWorks and returning risk indicators into the scheduling workflow for prioritization by patient access teams. Operational coverage was at the FQHC sites in Baltimore City and was positioned inside clinical operations and patient access business functions.
Governance and workflow changes included new triage rules for high-risk flags, updated scheduling protocols for outreach and appointment recovery, and operational monitoring by patient access leadership. The FQHC reported a 34% decrease in the no-show rate for high-risk patients and recovery of 309 appointments within 45 days of deployment.
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Healthcare | 865 | $141M | United States | eClinicalWorks | eClinicalWorks healow No-Show Prediction AI Model | Predictive Analytics | 2023 | n/a |
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