Identify the problem before it becomes the crisis.
For more than 40 years, my work has focused on using healthcare data, statistics, and database systems to find emerging problems before they turn into larger and more expensive ones.
Today, eResearchPro brings that same statistical and database work, now joined with a private AI model that runs on your own systems, to community hospitals that want to understand their financial, operational, and patient-risk patterns using the data they already own.
Three questions every hospital should be able to answer
Where are we beginning to lose money?
Identify emerging patterns involving denied claims, length of stay, staffing expense, utilization, readmissions, and other sources of financial pressure.
Who is becoming expensive?
Identify the patients and populations developing the greatest utilization, clinical, and financial risk before those costs become larger.
What is getting worse?
Detect changes in clinical, operational, and financial patterns that could become significant problems if the trend continues.
Financial risk often develops before it becomes obvious
A hospital already generates much of the information needed to identify developing risk. The challenge is connecting those data and detecting meaningful changes early enough for management to investigate and respond.
Early warning, not another reporting system
Traditional reports often tell management what has already happened. Our objective is to identify patterns that are changing now and may deserve attention before they become larger financial or operational problems.
We combine available clinical, operational, claims, and financial information and apply statistical analysis, risk modeling, comorbidity measures, and a private AI model where it adds value.
The technology is not the product. The product is earlier warning and better information for management decisions.
How we are different from other vendors
Many firms sell hospital analytics, and most do competent work. They also tend to share the same limits. Here is where our approach differs.
| Most analytics vendors | eResearchPro |
|---|---|
| Report what is happening through dashboards and benchmarks. | Explain why it is happening, and which part of it you can actually change. |
| Send patient data to a cloud platform or an outside service. | Run a private AI model on your own servers, so patient data never leaves the building. |
| Sell software licenses and subscriptions. | Sell the analysis itself. The tools are open-source or already licensed, and you keep what we build. |
| Hand over a report and move on. | Stay in the conversation and translate the findings into plain language your staff can act on. |
| Stop at what is associated with cost. | Use statistical methods that separate patient complexity from the factors you can control. |
Reasons to hire us as your consultants
The work is practical, the commitment is small to begin, and the capability stays with you.
The analysis is done by a scientist with more than 40 years in healthcare data, not handed down to junior staff working behind a platform.
We both build the data and analyze it. The same hands that engineer a SQL Server data mart also run the statistics, so nothing is lost in a handoff between teams.
Using causal methods, statistical tools that test whether one thing is actually causing another rather than simply appearing alongside it, we identify the cause behind a financial pattern.
Our model reads the clinical notes your reports cannot reach and runs entirely on your systems. No patient information goes to an outside service.
When the engagement ends, the data mart, the model, and the methods stay with you. You are not renting a platform you have to keep paying for.
Begin with a fixed-fee risk scan on a limited dataset, see the results, and decide from there. There is no large commitment to begin.
Deep statistical and database expertise
Most hospital analytics stops at counting and comparing. Our work goes further, because the person leading it has spent decades on both sides of the problem, the databases that hold the data and the statistics that make sense of it.
SQL Server design, data marts, and the work of pulling messy clinical, claims, and financial data into one place that can actually be analyzed. Decades of C#, Python, and SQL Server behind it.
Biostatistics, comorbidity measures such as the Charlson index, and causal methods that separate the real drivers of cost from differences in patient mix.
Natural language processing to pull meaning from the free-text notes that structured reports cannot use, with privacy-preserving de-identification throughout.
A private AI model that stays on your systems
Hospitals are right to be cautious about sending patient information to a public AI service. Our approach keeps both the model and the data inside the building. A private language model runs on hospital-controlled hardware, reads the clinical notes your reporting tools cannot touch, and sends nothing to an outside vendor.
We sell the service, not the software. The tools we use are open-source or already licensed, so you are not buying another platform or subscription.
The analytical environment operates on hospital-controlled infrastructure rather than requiring patient data to be sent to a public AI service.
Data access can be read-only and limited to the agreed project, within the hospital's existing security, privacy, and compliance requirements.
Hospitals can obtain senior analytical capability without first recruiting and building a complete internal data-science team.
An example: finding the patients who drive the cost
In one analysis of a statewide Medicaid population of about 112,500 patients, we used the Charlson Comorbidity Index, a score that combines a patient's chronic conditions into a single measure of complexity, to flag each patient as higher or lower mortality risk. We then connected that risk flag to where charges actually concentrated.
The result showed what many hospitals suspect but cannot easily prove. A small share of patients carried a large share of the total cost, and the same high-complexity group could be identified early from data the hospital already held. We also reported how well the risk flag performed, including where it fell short, because an honest measure is worth more than a flattering one.
The same approach applies to your own population. We find the small group that drives the cost, then separate how much is the illness itself from what can be changed.
Start with a Hospital Financial Risk Scan
Do not begin with a large technology project. Begin with a defined financial question and your own data.
We start with a small, fixed-fee analysis using a limited dataset. Before beginning, we agree on the questions to be answered, the data required, and how the usefulness of the analysis will be judged.
The result is an executive-level view of where risk appears to be developing and where additional investigation may provide the greatest value.
More than 40 years of experience
eResearchPro is led by Alfred A. Cecchetti, Ph.D., MSc, MSc IS, whose work spans more than four decades in clinical and research informatics, biostatistics, database systems, and healthcare analytics.
That work combines data engineering, statistical and causal analysis, comorbidity and risk modeling, natural language processing, and private on-premise AI, so a hospital can turn the information it already holds into practical management intelligence without sending data to an outside service.
Dr. Cecchetti holds a doctorate in information management and master's degrees in biostatistics and information science, and has directed university research data and analytics operations.
What is getting worse at your hospital?
A first conversation can focus on the two or three financial or operational risks that concern your management team most.
cecchetti@eresearchpro.com724-681-2208