Healthcare today still relies heavily on digital records that sit idle. Hospitals generate vast amounts of patient andoperational data; by estimate (World Economic Forum), about 50 petabytes each year. Yet, they use only about 3% of it. In practice, most of this data is not used for decision-making. Research shows that roughly 47% of clinical and business data goes unused. A typical hospital IT service might provide flashy dashboards, but busy clinicians and managers often find them static and disconnected from real-time needs. It takes more time to build these dashboards than to use them. Hospitals have digital documentation systems but lack the analytics or execution layers that turn data into action.
The hard truth is that most hospitals use their digital systems as fancy filing cabinets. They document, store, and generate reports, but they rarely act—neither automatically nor in time, and not at the level of detail needed to change outcomes or improve operations.
This is the gap Sehmr, the AI-powered hospital platform from Stairway Technologies LLP, aims to fill. It offers more than just better dashboards or fancier charts. It is a true execution system that turns dormant hospital data into real-time intelligence, automated workflows, and decision support exactly when and where clinicians and administrators need it.
A 2022 study by McKinsey and Harvard researchers found that the healthcare industry could save up to $360 billion annually after AI is widely adopted, just in the U.S. These unintended savings, which do not come at the expense of clinician or patient experience, mean money can be invested in the many other struggling areas of healthcare.Electronic health records (EHRs) and hospital information systems (HIS) transformed the practice of creating and storing health records. However, similar to their paper based predecessors, EHRs and HIS were built primarily to facilitate the documentation of health interactions. As time has shown, these systems were built to record the prescription, diagnosis, and discharge summary. However, these systems were never intended to perform the act of reasoning. These systems were never intended to analyze, flag or perform acts pro-actively. The inability of these systems to perform acts reasoning is what we have termed the 'execution gap.' The 'execution gap' is the gap between the knowledge and information stored within a hospital system, and the information that clinicians and health executives are actually presented with. A typical EHR would know that three patients within the same ward, with similar presentations, were prescribed with the same antibiotic within the past three days. However, would a typical EHR notify the Infection Control team about it? The answer is most likely no across all hospital systems. A clinician is expected to see the information, escalate it and undertake an investigation
1. Healthcare data underutilised for decisions: 47% (Source: Arcadia / HIMSS, 2024)
2. EHR data that is unstructured (notes, images, audio): ~80% (Source: FormX / IBM, 2024)
3. Healthcare analytics market size (2024): $35–53 Billion (Source: Multiple research firms, 2024)
4. Projected market size by 2032–2034: $200–364 Billion (Source: Grand View / Market.us, 2025)
5. Compound annual growth rate (CAGR): 21–25% (Source: Databridgemarketresearch, 2024)
6. AI-related hospital job postings vs total hospital jobs: <0.1% (Source: Healthcare job survey data)
7. Hospital leaders planning to integrate AI/ML: 84% (Source: Arcadia / HIMSS, 2024)
These numbers paint a stark picture. Healthcare is one of the most data-intensive industries on earth — and also one of the least effective at acting on that data. The analytics market is exploding not because hospitals have too much insight, but precisely because they have so little of it. The investment is finally catching up to the need.
From Static Reports to Dynamic Intelligence A manager sits down with a spreadsheet or a printed MIS report every morning under the typical hospital reporting methodology. They look through patient counts, revenue data, and bed occupancy rates. They escalate if anything doesn't seem right. They move on if everything appears to be in order. The sluggish, labor-intensive, and retroactive nature of this procedure is the issue.
The data in a hospital is always changing. Patients get worse. Clusters of infections appear. Revenue collection declines. Chains of supply break. Numerous little choices that may have been made in the moment have already been overlooked by the time a morning report shows yesterday's figures.
This changes the whole idea upside down: there are no mountains of data for anyone to sift through. Sehmr operates on a very simple yet powerful principle: Show relevant information when it is relevant to the decision-making process.
For instance, if today's collection of data for hospital owners is well within the expected range of variation, they receive a green light, nothing else. No reports; just a green light showing all is fine.
● If any abnormal situation arises, such as a drop in income, an unusual trend in laboratory values, an abnormally low occupancy rate in some ward – this will be flagged up automatically, complete with contextual information and actionable recommendations.
● If a patient is showing trends indicating he is approaching some critical point, the responsible clinician is alerted to act before it happens.
● If there is a trend suggesting emergence of some disease outbreak, Sehmr will detect it without delay – a feat that was simply impossible to accomplish via conventional HIS systems without dedicated surveillance.
In short, documentation system and execution system – this is the distinction. Benefits of AI-based Outbreak Detection Systems: According to research published in clinical literature, AI-based algorithms for detecting outbreaks may predict
● Data Utilization: Legacy systems act on ~3% of data, whereas Sehmr utilizes a full data pipeline for real-time insights.
● Clinical Alerts: Legacy systems require manual review; Sehmr provides automated, context-aware alerts.
● Sepsis Detection: Legacy systems are retrospective and delayed; Sehmr offers early prediction up to 48hours in advance.
● Documentation: Legacy systems require clinicians to type everything; Sehmr's AI scribe saves 16-27 minutes per day per doctor.
● Outbreak Detection: Legacy systems rely on manual surveillance; Sehmr automates pattern detectionacross patients.
● MIS Reporting: Legacy dashboards have low utility; Sehmr provides outlier-only alerts where "green"signifies all clear.
● Operational View: Legacy systems use static reports requiring an analyst; Sehmr offers real-time tracking ofwards, census, and revenue.
● Research Tool: Legacy systems have limited or no manual queries; Sehmr includes an in-built researchengine for clinical queries.
● Hospital Owner View: Legacy systems require a dedicated manager; Sehmr provides smart summaries andexception-based alerts.
● Workflow Automation: Legacy steps are human-driven and manual; Sehmr utilizes AI agents to automateend-to-end workflow
The commercial landscape itself is confirming what clinicians and administrators have been saying for some time: documentation alone does not meet the needs of healthcare organizations. The global healthcare analytics market illustrates this fact.
Numerous research companies have estimated that the market was valued at roughly $35-$53 billion in 2024, with projections this size will expand between $200 billion and $364 billion by 2032-2034, or at an estimated CAGR of 21%-25%. This difference in growth is more than just incremental; it represents a change in how hospitals will do business.
According to the results of the 2024 Arcadia/HIMSS survey of healthcare leaders, 84% plan to incorporate AI, machine learning, or large language models into their data platforms. The "95% EHR Adoption Paradox:" As of 2024, 95% of U.S. physicians practicing in an office setting have adopted EHRs (National Electronic Health Records Survey, 2024). However, 47% of the data that these EHR systems produce go unused when it comes to making decisions. Therefore, this is not an EHR adoption problem; it is an execution problem where simply implementing an EHR system is not sufficient for developing an execution system.
The healthcare industry creates an amount of data. In fact it creates data than almost any other industry in the world. This data is very important because it can help save lives reduce the time doctors and nurses spend on paperwork, lower costs and improve the way patients are treated. The problem is that for a time hospitals have been using systems that are good at storing data but not very good at using it.
Sehmr is different. It is not a new way to look at data. It is a system that helps hospitals use data to make decisions. It uses intelligence to help doctors and nurses do their jobs. It shows them the information they need to see. This helps hospitals to stop reacting to problems and start preventing them from happening in the first place. There is a gap, between the data hospitals have and the way they use it.. Sehmr can help close this gap. For hospitals that're ready to start using data to improve patient care Sehmr is a good place to start.
