Is your healthcare organisation struggling to manage huge amount of patient data? Do you think meaningful insights from this data could help reduce costs and save more lives? In simple terms, data analytics in healthcare means collecting and examining clinical, operational, and financial data, from electronic health records to insurance claims, to uncover patterns that improve patient outcomes and control costs. Data analytics is a fast-growing discipline in the healthcare industry, with benefits in the form of saving time and costs, as well as optimising processes and saving countless lives. It reduces the cost of treatment and predicts epidemics and life-threatening diseases on time. With such immense benefits, the global healthcare analytics market is set to increase from $65.6 billion in 2025 to $198.8 billion by 2033 at a CAGR of 13.5%.
Types of data analytics in healthcare
Not all healthcare analytics work the same way. Depending on what a hospital or health system needs to know, data analytics in healthcare falls into four broad types:
- Descriptive analytics: Looks at past and current data, such as patient volumes, readmission rates, or billing trends, to understand what has already happened and spot patterns.
- Diagnostic analytics: Digs into why something happened, for example examining the root causes behind a spike in emergency room wait times or a cluster of surgical complications.
- Predictive analytics: Uses historical and real-time data to forecast what's likely to happen next, such as which patients are at higher risk of readmission or when a facility should expect a surge in demand.
- Prescriptive analytics: Goes a step further and recommends a specific action, for example adjusting staffing levels or treatment plans based on what the data suggests will produce the best outcome.
The applications covered in this article, from forecasting operating room demand to preventing readmissions, draw on all four types working together.
Importance of business analytics in healthcare
From diagnostic imaging to administrative and billing systems, data analytics is transforming the overall healthcare industry. The data analytics trend accelerated during the COVID-19 pandemic, which revealed the gaps in the current system. Data analytics in healthcare helps doctors predict patients’ medical interventions and treatments based on their age, medical history, and possible scenarios and outcomes of medication. Data analytics also helps hospitals in managing their resources better, such as increased demand for medical equipment, beds, and staff during surges in patient volume, as well as helping healthcare institutions provide tailored treatments to patients, with data and AI-based systems doing the heavy lifting and delivering actionable insights. This is exactly why business analytics in healthcare industry settings has moved from a back-office reporting function to a core part of how hospitals plan staffing, budgets, and patient care.
Data points in the healthcare industry
Hospitals operate and manage a wide range of clinical and operational information systems. While this is not an exhaustive list, here are some of the data points:
- Electronic health records (EHRs): Clinical documentation, reporting test results, patient medical history, and patient orders
- Laboratory information system (LIS): The laboratory information system that interfaces with the EHRs
- Diagnostic and monitoring: The vast inventory of diagnostic and monitoring equipment for magnetic resonance imaging, vital signs, and test result interpretation, which may or may not interface with the EHRs
- Insurance claims and billing: Information on a patient’s treatment, the costs, and expected payment as well as information in the EHR that determines the level of service
- Pharmacy: Data on medicines and other supplies sourced from the hospital pharmacy
- Human resources and supply chain: Data about employees and their role within the medical institution, which also includes the location and utilisation of medical supplies
How is data analytics used in healthcare?
Data analytics and visualisation can increase a patient’s access to services and lower costs. Here are the major applications of data analytics in healthcare:
- Manage patient health records: Hospitals need to digitise medical records for substantial cost savings and extract data from administrative and diagnostic systems to update them in real time.
- Forecast operating room demands: Operating rooms are expensive to build and maintain. Data analytics helps hospitals optimise the cost without impacting patient care. This demands a thorough understanding of the relationships between operating room variables for better scheduling.
- Optimise staff: Accurate staffing is important to save costs and time. Data analytics help hospitals predict and deal with staff challenges based on local weather trends, holidays, and seasonal infections in advance.
- Prevent 30-day hospital readmissions: Data analytics prevents unnecessary readmissions in a 30-day window to reduce costs and ensures availability for patients who need immediate care.
- Predict no-show appointments: Patient no-shows have financial ramifications and throw doctors off their schedule. Data analytics can predict no-shows to improve staffing and reduce wait times.
- Manage supply chain costs: Hospitals rely on massive supply chains, and delivery times are critical. Data analytics maintains efficiency by tracking supply chain metrics, thus saving lives and costs.
- Enhance security and prevent fraud: Cybercriminals attack hospital databases resulting in the loss of revenue and leakage in confidential information about patients. Data analytics identifies patterns and changes in network traffic to detect suspicious online behaviour.
- Reduce medical errors: Medical errors in surgeries, diagnosis, and medication affect over 400,000 patients annually. This happens due to a lack of information or negligence by the staff. Data analytics reduces such errors by flagging anything that seems out of place.
How can Infosys BPM help with data analytics in healthcare?
Using our strong domain expertise, flexible operating models, and an integrated IT-BPM approach, elevate your business’ performance and reduce expenses. Read more about Infosys BPM healthcare solutions. From understanding what data analytics in healthcare industry teams need day to day to helping hospitals act on it, our approach is built around practical, measurable outcomes rather than analytics for its own sake.
Frequently asked questions
Health informatics builds and manages the systems that capture and move clinical data, such as electronic health records and the interfaces between them. Healthcare analytics interprets that data to answer clinical and operational questions. Hospitals need both: informatics keeps the data complete and reliable, analytics turns it into decisions.
It can be, when the programme is governed properly. Analytics teams typically work on de-identified or access-controlled data, under health-privacy regulations such as HIPAA in the US and equivalent laws elsewhere. The safeguards that matter are role-based access, audit trails, and de-identifying records before analysis, not the analytics itself.
The usual blockers are data silos between clinical, billing, and laboratory systems, large volumes of unstructured notes and images, a shortage of staff who can interpret analytics, and clinicians who are not shown how insights fit their workflow. Fixing data quality and ownership first usually pays off more than adding advanced models.
By setting a baseline before deployment and tracking the change afterwards, using metrics such as readmission rates, average length of stay, claim denial rates, staffing overtime, and no-show rates. Most of the return shows up as avoided cost rather than new revenue, which is why the baseline matters.
Smaller practices benefit too, usually through cloud-based tools or an outsourced analytics partner rather than an in-house team. Scheduling, no-show prediction, and billing accuracy are the common starting points, because they need little infrastructure and show results quickly.
