Healthcare

Eight ways to use data analytics in healthcare

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? 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 $23.51 billion in 2020 to $96.90 billion by 2030 at a CAGR of 15.3%.

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.

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

Ways to use data analytics 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:

1. Manage patient health records: Hospitals need to digitise medical records for substantial cost savings and extracting data from administrative and diagnostic systems to update them in real time.
2. 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.
3. 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.
4. 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.
5. 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.
6. Manage supply chain costs: Hospitals rely on massive supply chains, and delivery times are critical. Data analytics maintains the efficiency by tracking supply chain metrics, thus saving lives and costs.
7. Enhance security and prevents 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.
8. 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.

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