Most healthcare data science projects stall before a model ever reaches a clinician. The data sits in Epic, claims files and device feeds nobody has reconciled, and a notebook result rarely survives compliance review. Taction Software builds HIPAA-compliant data science that starts from your real EHR, FHIR and X12 data and ends inside the workflow where decisions are made. Since 2013 we have delivered 200+ healthcare projects, with a BAA signed before the first commit.
Start with a Discovery Sprint: we assess your data, pick the use case with the clearest return and scope the model before you commit to a build. Or get a project estimate.
Our experts are ready to understand your business goals.
Six service lines, each built around a decision someone in your organization makes every day. We scope against the decision first, then choose the data and the model.
We build models that flag readmission risk, deterioration and no-shows early enough to act. Each model is trained on your own population, validated against held-out data and calibrated so scores mean the same thing month to month. See our predictive analytics work.
Much of the clinical story lives in notes, discharge summaries and pathology reports, not structured fields. Our clinical NLP development extracts diagnoses, medications and social risk factors from free text and maps them to SNOMED CT, ICD-10 and LOINC for downstream models.
For ACOs, health systems and payers, we segment populations by risk, cost and care gaps so care teams know who to call first. Models combine claims, EHR and SDOH data into one patient view. See population health analytics and SDOH analytics.
Denials, underpayments and slow A/R are pattern problems, and data science finds the patterns. We model denial likelihood before submission, trace root causes by payer and CPT code, and score accounts for collection priority. Related: denial trend analytics and revenue cycle analytics.
We train and validate computer vision models on DICOM images for detection, segmentation and triage, with labeling protocols your radiologists approve. Models run beside PACS rather than replacing it, and we plan the FDA SaMD pathway early when it applies. See computer vision for medical imaging.
Models are only as good as the pipeline behind them. We build healthcare AI data pipelines on Snowflake, Databricks or a healthcare data lake, with OMOP mapping, data quality checks and feature stores that keep training and production data consistent.
Healthcare data science fails at the source more often than at the model. We have integrated the systems below in production, so we know where their data breaks.
We pull structured and unstructured EHR data through FHIR APIs, Clarity and Caboodle extracts, or HL7 feeds, depending on what your contract allows. Our Epic integration and EHR integration teams handle access, so data scientists start from clean, documented tables.
Real-time ADT, ORU and order messages make models timely: a deterioration score is only useful while the patient is still on the unit. We stream HL7 and FHIR data through Mirth Connect into feature pipelines and write scores back as FHIR resources.
Claims show what happened across every care setting, not just inside one EHR. We parse 837 and 835 files, normalize payer codes and link claims to clinical records for cost, utilization and denial models. Background: our X12 837 and 835 guide.
Continuous readings from blood pressure cuffs, glucose monitors and wearables arrive noisy and incomplete. We clean, align and summarize the streams so models detect real deterioration trends instead of sensor gaps and false alarms, building on our remote patient monitoring software work.
The same model type solves different problems for different buyers. Here is where we usually start with each.
Hospitals usually start with readmission, length-of-stay and capacity models, because the operational impact is easy to measure and easy to defend at a board meeting. We build 30-day readmission prediction and length-of-stay analytics that care managers and bed management teams use in their daily huddles.
Payers use data science to predict high-cost members, detect fraud, waste and abuse, and close HEDIS gaps before the measurement year ends. We build models on claims, pharmacy and authorization data, including payer fraud detection that ranks suspicious claims for investigators.
Digital health teams need models that prove clinical value to buyers and investors. We help you choose an outcome you can measure, build the model into your product and produce the validation evidence that enterprise customers ask for. See AI for digital health startups.
Research teams use our pipelines for real-world evidence, cohort discovery and trial feasibility, where timelines depend on finding eligible patients fast. We map EHR data to OMOP so studies run across sites with one query, and build real-world evidence platforms with OMOP implementation underneath.
Compliance is where most healthcare data science projects stall. We build the controls in from the first sprint, so your privacy and security teams review a design, not a finished system.
We de-identify data under HIPAA Safe Harbor or Expert Determination before it reaches a training environment, and redact PHI from free text at ingestion. Where real data cannot leave your network, we use synthetic data generation for development. Details: PHI redaction services.
Every model ships with documentation of its training data, performance by subgroup and known limits. We test for bias across age, sex, race and payer, monitor drift in production and log every prediction, aligned with our healthcare AI governance framework.
We sign a BAA before the first commit and work inside your cloud tenant or a HIPAA-compliant AI hosting environment. Access is role-based, encrypted and audited, and our delivery runs under ISO 27001 controls, so security questionnaires get answered with evidence.
Every engagement follows our PROOF framework: five phases that move a model from a first prototype to a governed production system, with a go/no-go decision at each gate.
We start with one decision and the data behind it. In a short discovery phase we profile your data, confirm the use case has enough signal and build a baseline model, so you see real performance numbers before committing budget to a full build.
We improve features, tune the model and validate it with your clinicians on cases they recognize. This is where we cut false alarms, set thresholds that match staffing reality and agree on the metric the model will be judged by in production.
The model goes live inside the workflow: an Epic BPA, a worklist, a dashboard or an API your product calls. We deploy with healthcare MLOps practices, including versioning, rollback and silent-mode testing before any score reaches a clinician or care manager.
We monitor accuracy, calibration and drift against live outcomes, and track whether staff act on the scores. A model nobody uses is a failed model, however accurate it looks. When performance slips or practice patterns change, retraining is triggered with documented evidence, not guesswork.
We hand over model cards, validation reports, runbooks and governance records your compliance team can file. Your staff are trained to run and retrain the model, or we stay on under a support agreement. Nothing stays locked inside a vendor’s black box.
We are a healthcare software company that does data science, not a data science shop learning healthcare. The difference shows up when a feed breaks or an auditor asks how a score was produced.
Since 2013 we have delivered 200+ healthcare projects for hospitals, payers, RCM firms and digital health companies. Our data scientists work beside the engineers who built those systems, so they understand clinical workflows, coding rules and the operational constraints that shape every model.
Getting data out of an EHR is half of every data science project. Interoperability is our core work: Epic, Oracle Health, HL7, FHIR and Mirth Connect interfaces are built by the same team, so data access does not stall the project. Explore our healthcare interoperability work.
A model in a Jupyter notebook helps nobody. We measure success by scores used inside clinical and financial workflows, and each build ends with a deployed, monitored system. See the results in our healthcare case studies, including predictive cardiac RPM.
Run a fixed-scope project, add a dedicated data science pod, or extend your team with specialists from discovery through production support. If you need individual experts, you can also hire healthcare data scientists or ML engineers who already know HIPAA and EHR data.
Bring one decision you want to improve: readmissions, denials, no-shows or something specific to your organization. In a 30-minute call we will tell you whether your data can support it and what a first model would take.
Answers to the questions buyers ask most before a data science engagement.
They cover the full path from raw data to a decision: data access and engineering, feature design, model building, clinical validation, deployment into workflows and monitoring. At Taction Software that includes predictive models, clinical NLP, computer vision and claims analytics, all delivered under a BAA.
Analytics describes what happened, through dashboards and reports. Data science predicts what will happen and recommends what to do, using statistical and machine learning models. Most organizations need both; our healthcare data analytics team covers reporting, while this practice builds the predictive layer.
A discovery and baseline model usually takes a few weeks. Taking a model from prototype to production typically runs 12 to 24 weeks, depending on data access, the number of sources and whether the model needs clinical validation or FDA review. We confirm the timeline after discovery.
Yes. We can deploy inside your cloud tenant or on-premises environment, train models where the data lives, and use de-identified or synthetic data for development. Your PHI does not need to move to our systems for a project to succeed.
We work with Epic, Oracle Health (Cerner), athenahealth, MEDITECH, eClinicalWorks and NextGen, and with data platforms including Snowflake, Databricks, AWS, Azure and Google Cloud. Data arrives through FHIR, HL7 v2, X12 EDI, flat files or direct database extracts. Each source is documented before modeling starts.
Book a Discovery Sprint. We review your data sources, rank candidate use cases by value and feasibility, and deliver a scoped plan, often with a baseline model. If the data cannot support the use case, you learn that before a full build.
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Our expert reaches out shortly after receiving your request and analyzing your requirements.
If needed, we sign an NDA to protect your privacy.
We request additional information to better understand and analyze your project.
We schedule a call to discuss your project, goals. and priorities, and provide preliminary feedback.
If you're satisfied, we finalize the agreement and start your project.