Does your health organisation face difficulties harnessing a vast amount of patient data? Do you believe realisable insights from these data could help you reduce costs and save more lives? In the healthcare sector, the fastest-growing line of business is data analytics, with examples of saving time, saving costs, optimising processes, and saving numerous lives. It reduces treatment costs while forecasting an outbreak of epidemics and serious diseases. With such far-reaching advantages, the global healthcare analytics market is set to rise from USD 23.51 billion in 2020 to USD 96.90 billion by 2030, at a CAGR of 15.3%.

How is data analytics being applied in healthcare?

Data analytics and visualisation can open up access to patient services and reduce costs on the operational side. Following are important applications in healthcare for data analytics:

1. Managing patient health records: In order to avoid massive costs from storage, hospitals need to digitise medical records and extract data from boards and diagnostic systems to update them in real-time.
2. Operating room demand forecasting: The raison d’être for operating rooms is to optimise costs while ensuring that patient care is not compromised. This requires an understanding of the interactions between the multiple variables of the operating theatre to enhance scheduling. Operating rooms represent huge investments to build and maintain, and data analytics can ensure the patient is cared for whilst optimising costs.
3. Optimise human resource utilisation: Predicting human resource challenges with an accurate understanding of staffing will save costs and time. Through data analytics, hospitals are able to forecast and address workforce issues in advance related to local weather trends, holidays, and seasonal infections.
4. Avoid 30-day hospital readmissions: Data analytics help avoid unnecessary re-hospitalisations within 30 days to reduce costs and make beds available for patients needing prompt care.
5. Predict no-show appointments: Patient no-shows carry a financial burden and cause undue disruption to a physician’s schedule. Data analytics can forecast no-shows, allowing for maximal efficiency in staffing and potentially reducing wait times.
6. Manage supply chain costs: Hospitals utilise massive supply chains for service delivery, and timely delivery is of the essence. Data analytics keeps track of supply chain parameters so as to optimise operational efficiencies that save lives and money.
7. Improve security and anti-fraud: Hospitals are ever-vulnerable to cybercriminals who invade their databases, leading to loss of revenue and leakage of confidential information related to patients. Data analytics identifies patterns and detects suspicious online behaviour by monitoring and analysing changing trends in network traffic.
8. Minimise medical errors: Medical errors within surgeries, diagnoses, and medications take a yearly toll on over 400,000 patients. This happens due to lack of information or even a careless approach on the part of the staff. Data analytics tries to eliminate the occurrence of such errors by putting a flag on anything that looks even slightly improper.

Some companies transitioning through digital transformation rely on agility to respond to various technologies and rapidly changing business environments. Expectations should be exceeded with an innovation-driven digital mindset more than ever before. It would be extremely important for companies to know how to sense, learn, respond, and evolve, almost as if they were living organisms, to deliver excellence to businesses. This is where a fully integrated but modular suite of services comes in. Providing organisations with real-time visibility over their functions for hyper-productivity via intuitive, scaled automated decision-making, action-based insights, and anywhere, anytime experiences, 

Data points in the healthcare industry

Hospitals operate and manage a slew of clinical and operational information systems. While the following list is not exhaustive, we provide just some data points:

1. Electronic health records (EHR): Clinical documentation, medical history of the patient, ordering tests, reporting test results, and patient orders
2. Laboratory information systems (LIS): The lab information system interfaced with the EHR.
3. Diagnostic and monitoring: A mostly extensive array of diagnostic and monitoring devices, i.e., MRI, vital signs, and test results interpretation, interfacing or not with the EHR
4. Insurance claims and billing: Information on the treatment rendered to a patient, costs, and anticipated payments, as well as information in the EHR that would justify the level of care
5. Pharmacy: Data regarding pharmaceuticals and other products flowing from the hospital pharmacy
6. HR and supply chain: Employee data and their relationship to the health institution, including the whereabouts and use of medical supplies

Benefits of Data Analytics in Healthcare

Continue reading to explore the many benefits of data analytics in healthcare and learn about different courses at London’s premier hub of training and consulting, which equips attendees with the skills and knowledge needed for successful careers in this dynamic field.

1: Enhancing Patient Care through Data Insights

The very essence of health care is the service to humans. Data analytics has benefits for patient care at both levels: macro and micro levels. In addition, use of big data should be extended to healthcare operations for such things as:

1. Personalised treatment plan: Personalised patient data take into account specific genetics and medical history as well as lifestyle and environment in suggesting the most suited treatment plan.
2. Predictive care: Using previous and current trend data to predict medical problems which can otherwise hasten the early detection and prevention of epidemic spread of diseases.
3. Faster diagnostics: data analytics solutions further hasten treatment decisions made by healthcare professionals as well as the diagnosis.
4. Enhanced development of treatment: Data insights speed up research, thus resulting in a faster discovery. Patient care benefits include the management of chronic conditions as well as time savings surrounding wasteful testing.

2: Cutting Healthcare Costs with Data Analytics

Not that healthcare is new and expensive. All sorts of prices accrue between medicines and hospital stays. Then healthcare data analytics enters this: it’s for saving money that systems and operations are streamlined or improved. With healthcare data analytics, it can:

1. Assist in more efficient allocation of resources
2. Help promote early intervention
3. Decrease unnecessary tests
4. Decrease readmission rates
5. Optimise staffing
6. Reduce operational costs
7. Integrate care in chronic conditions

Patients will be getting the lower costs, but waste will, most probably, disappear in healthcare organisations if not transform completely.

3: Enhancing Operational Efficiency in Healthcare Facilities

Data analytics is that touch of exceptionalism which has a strong bearing on performance and management in any health facility. Productivity and unity of process within a successful organisation stand on the foundation of health data insights. More so, data insights can do the following for such healthcare organisations and facilities:

1. Enhance scheduling: Keep track of staff shifts, patient appointments, room bookings, and much more.
2. Automate inventory: programme the system to order crucial supplies when healthcare organisations and facilities are running low, not once they desperately need them.
3. Optimise workflow: coordinate electronic health records, admission, discharge, and patient communications.

Using real-time data to dynamically adjust resources within health organisations leads to better productivity overall.

4. Enhancing Decision-Making for Healthcare Providers

In healthcare, time of decision-making may equal life or death. In less intense scenarios, healthcare providers ought to act swiftly and surely in their decisions to render diagnosis and treatment without resource wastage.

With the aid of data analytics, healthcare personnel are finding actionable clinical insights, thereby delivering quality patient care.

EHRs are another critical means of speeding up decision-making. Data-driven EHRs boost diagnostic and therapeutic pathways via patients’ medical history, lifestyle, and other impacting factors. In addition, record digitisation allows for systematic storage, easy access, and transfer among various hospitals and clinics.