Big Data Analytics has already transformed the business world, but its biggest impact may soon be seen in the field of medicine as the healthcare industry transforms its approach to patient care, physician performance, public health and even budgeting.
Positive effects are already seen through advancements in telemedicine (particularly during the COVID-19 pandemic), patient engagement, disease prevention, staffing efficiency and opioid abuse prevention.
As an immense amount of healthcare data is being generated and collected daily, savvy healthcare data analysts are finding new ways to turn expanding databases into positive outcomes. Analytics professionals are already using data visualisation to identify and control disease outbreaks (e.g. the monkeypox epidemic), speed up clinical trials, improve genomics research, and analyse socio-economic determinants of health to develop targeted disease prevention programs.
What is Healthcare Data Analytics?
Healthcare analytics is the process of analysing current and historical data in order to predict trends and improve service delivery, both at a macro and micro level. Data (if combined with business intelligence suites and data visualisation tools) can reveal numerous paths to improvement in patient care, diagnosis and even business management through the delivery of actionable insights.
Due to increasing digital data collection, more and more health care data is collected and available for analysis in real-time. For hospitals, clinics, and healthcare managers, healthcare data analytics can leverage financial and administrative data along with information about patient care and sentiment, physician performance and procedures.
Which Healthcare Data Is Used?
Data is collected on the processes and procedures of the business and administrative side of the healthcare sector, as well as actual health data regularly collected and stored by medical practitioners and researchers. Health data relates to the health of an individual patient or the collective patient population through health information systems (HIS), insurance companies and government organisations. Information that has been collected can be broken down into specific datasets for analysis.
Tools and systems used to collect, store and analyse this information can include Electronic Health Records (EHRs), Personal Health Records (PHRs), Electronic Prescription Services, Master Patient Indexes, Patient Portals and even health-related smartphone apps.
Healthcare Data Analytics professionals may assist researchers, physicians and hospital administrators with the end-to-end analytics process or build analytics tools with simple graphical interfaces that these clinicians and administrators can use without a background in analytics.
Practical Applications Of Healthcare Data
There are several ways data is practically applied in different fields of healthcare and medicine:
In clinical settings, information about patient care is used to reduce patient wait times, improve scheduling, staffing and customer service, and reduce readmission rates through the analysis of population health information;
Public health professionals/population health managers use information including lab testing, claims data and surveys to prevent and predict high-risk patients and populations;
Insurance firms use analytics to meet compliance regulations, analyse claims and compare pricing data to identify high-value, low-cost providers;
Various healthcare payers use predictive analytics to identify claims that represent a high risk for fraud.
In simple terms, healthcare analytics can be descriptive (drawing comparisons or discovering patterns in historical data), predictive (making predictions about future events), or prescriptive (presenting the best course of action based on predictive data). It’s helpful to visualise these different forms of analytics as answering questions:
Descriptive: What happened?
Predictive: What is going to happen?
Prescriptive: What should we do in response to what is going to happen?
The Benefits of Using Healthcare Data Analytics
Healthcare data management can provide crucial information whenever it’s needed, enabling decisions that ultimately lead to better care and healthier communities. Here are just a few of the benefits various role players in the health industry can gain by using healthcare data analytics:
Predictive modelling analyses current and historical data to make predictions about future outcomes, using data mining, machine learning and statistics that identify patterns. This can be used to predict treatment outcomes, the risk for chronic illness or self-harm, or even which populations are at greater risk of specific diseases, e.g. diabetes, COVID-19. This can be used for risk scoring in insurance, readmission prediction in hospitals and the prevention of infection and deterioration at the patient level. It is already being used for population health management, particularly with regard to predicting outbreaks of infectious diseases and making predictions about their impacts so that preventive and preparatory measures can be taken.
Streamlining and Reducing the Cost of Healthcare
Predictive and prescriptive analytics can provide administrators with detailed models and data for lowering costs without patient risk. It can also be used in reducing no-shows, managing supply chain costs, preventing equipment breakdowns and reducing fraud or drug-seeking behaviours.
The processes and organisational structures of healthcare providers can impact the quality of care patients receive as well as the likelihood of positive patient outcomes.
Using healthcare analytics enables clinics to improve employee scheduling, emergency preparation and compliance through processes that include:
Waste reduction, including eliminating inappropriate care, preventable care-related injuries, and failures to follow proven procedures;
Increasing hospital capacity by gaining insight into techniques to better manage demand for hospital beds and other healthcare resources;
Improved project management for cost controls, risk management and process improvement;
Identifying the resources required to implement and sustain process improvements.
Improving Patient Care
In addition to reducing costs in hospitals and insurance settings (which can be passed to the patients), healthcare analytics can bring major improvements in real patient scenarios:
AI can analyse huge amounts of data and spot patterns that can make predictions about various diagnoses. Machine learning models enabled teams to effectively structure questions and various terms in order to predict PTSD diagnosis in patients in Boston with 90% accuracy. As many of these patients are at risk of self-harm, speedier diagnoses can make a significant impact on their treatment and general wellbeing.
Hospitals were also to utilise AI to automate labour-intensive data and patient screening tasks to speed up the COVID-19 testing and results process and improve patient outcomes. The in-person testing process was sped by 700%, saving 86% of the patient testing time taken up by manual data entry.
Improving X-ray screening
Radiologists would normally have to read medical images to arrive at a diagnosis. Today, X-rays and diagnostic tests are being studied by AI to determine the most likely diagnosis. By analysing the results of thousands of imaging results and tests, AI is already becoming adept at spotting lung and breast cancer, leading to earlier detection and treatment.
The fight against the opioid epidemic
Analytics can be used to reassess prescribing practices and apply effective health management strategies to specific patient scenarios. Dashboard tools developed by the Rhode Island Quality Institute are used to share vital information for patient care across physicians and treatment centres, leading to a reduction in return visits to the emergency room.
Improving Medical Research
Data analytics can be applied to research efforts in many health-related areas. By collecting information from EHRs, medical records, and personal and public health records, researchers have access to rich and useful data. The creation of patient and disease repositories and clinical trial data can also help researchers identify approaches to improving the efficiency of clinical processes, share key learnings, and provide new insights into the causes of diseases through cohort studies and new insights into the causes of diseases.
Health business management stands a lot to gain from data analysis, including improvements related to the recruitment, retention, training and hiring of staff. Labour costs can account for up to 60% of hospital budgets, while an increase in demand may lead to increases in salaries and wages.
Data analytics can provide insight that allows hospitals to manage their labour costs without compromising patient care. In one instance, Hawaii Pacific Health saved $2.2 million in under two years by implementing visual representations of labour utilisation within the hospital. This gave managers greater insight into staff productivity, the cost-effectiveness of staffing decisions and the ability to adjust staffing ratios without impacting patients negatively. By using an automated labour management system, the time managers spent on the schedule was reduced from four hours to fifteen minutes and eliminated paper-based scheduling, which gave employees instant access to information related to their schedules.
The Challenges of Healthcare Data Analytics
Implementing healthcare data analytics – and deriving value from it – is not without its challenges. Data that is collected must be cleaned, complete, accurate and formatted for its appropriate use. In many instances, healthcare data is unstructured and available in multiple, heterogeneous formats. EHRs should be optimised to prevent data capture issues and cleansed and standardised data pooled in a data lake or repository to pinpoint the next best actions.
Organisations should also consider how data will be shared and whether or not data privacy considerations exist. Healthcare organisations should also consider whether or not they will be able to acquire in-house knowledge to make the most of their data. In most cases, it’s best to partner with a company that offers advanced analytics services that can provide accessible analytical tools and provide insights into their data.
How to Approach Data Analytics
Healthcare data analytics provides value and opportunities for improvement to nearly every aspect of health systems. Start with a specific, goal-driven business question to centre data initiatives around that targets a service line, process or market, such as:
How can we reduce waiting times by 10%?
How can we minimise no-shows?
How do we acquire one million new patients by 2025?
Next, start gathering disparate sources of data. Demographic information, patient satisfaction surveys, consumer data and clinical data tend to exist in silos, which is why healthcare data management tools like data warehousing may be the natural first step. This will enable the business to consolidate and overlay the datasets in a way that enables them to answer pertinent business questions. When this information is combined with the right BI solution that integrates data from a customer relationship management tool (CRM), physician relationship management tool (PRM) and contact centre, the consolidated data becomes highly actionable.
Healthcare data analytics is already making a substantial difference in patient care and the ability of healthcare providers to deliver cost-effective, engaging care solutions. The role will continue to grow and expand as more sources of digital data become available, and new tools and consultants enter the market, providing greater access and insight to medical professionals and administrators.
Many hospitals and clinics simply do not have the IT resources or funds to build these solutions in-house and manage the connectivity, security, integration and implementation thereof. It’s best to choose a professional tech partner that can work with existing data sources and infrastructure to access and leverage the power of data analytics.
Understanding the impact technology can have when applied to existing challenges, operational efficiency, and patient experiences is the first step. Choosing an analytics provider that can deliver a fast time-to-value solution and access to reliable data while taking a consultative approach to delivering those solutions is the key to success and implementing positive changes.
Healthcare organisations are increasingly leveraging big data analytics to enhance their insights into operations and patient care, streamline processes and reduce costs. This not only provides a competitive advantage but improves the health and wellbeing of the patients and communities they care for. As research around AI and other analytical tools keeps advancing, there is no time like the present to investigate a healthcare data analytics solution of your own.