Healthcare Analytics Use Cases & Benefits

The future of healthcare organizations will be driven by data and digital transformation. You would agree the recent global pandemic has only accelerated this pace of change. For healthcare CIOs, analytics is now a top priority in 2021 more than ever, especially as health information systems (HIS) harness the power of data to optimize every aspect of the care continuum.

It is now the time to switch to value-based care to improve patient experience at every point of delivery. And if you too want to leverage the power of data with healthcare analytics implementation, then this article is for you.

Table of Contents

• Why Healthcare needs Analytics
• Benefits of Analytics for Healthcare Providers & Payers
• Healthcare Analytics Use Cases
• Common Industry Challenges that Health Data Analytics can Solve
• Future of Data Analytics in Healthcare
• Estimating the Cost of Developing a Healthcare Analytics Solution
• Unlocking the Potential of Health Data Analytics with Rishabh

Advancing healthcare and improving patients’ lives requires both measurement and extensive analysis of large healthcare data sets. The valuable and actionable insights help physicians, researchers, medical specialty societies, pharmaceutical companies, and every other healthcare stakeholder to leverage and work on improvement areas.

With an integrated analytics and reporting solution, practitioners can:

• Drive better treatment outcomes by delivering data-based, personalized care solutions
• Reduce patient wait times by assessing & optimizing scheduling & staffing procedures
• Ensure patient satisfaction by streamlining processes for making appointments, providing referrals, and processing insurance
• Reduce the rate of readmissions by analyzing personal patient records against population health data to accurately predict patients at an increased risk of chronic illnesses
• Improve the patient experience with better communication and connected care
• Identify areas to cut costs and optimize resource allocation
• Eliminate human errors in case of prescriptions and dosages with automated alerts
• Make informed decisions about cash flow and cost management

As healthcare organizations shift from a fee-for-service to a fee-for-value model, the need to improve practice performance and patient care quality make data analysis a crucial function of daily workflow. 

For Healthcare Providers

Powered with core capabilities in technologies like artificial intelligence and machine learning, a smart solution can forge seamless connections across the healthcare ecosystem. By collecting & analyzing patient health data sets, providers can:

• Identify high-risk patients and accordingly allocate staff and resources for timely interventions and improved outcomes
• Bridge the intervention gap with inpatient care during epidemics by tracking patient intake and specific medical conditions
• Monitoring and measuring health outcomes to determine the success and efficacy of treatments and healthcare programs
• Measure practice performance, patient experience and other KPIs to facilitate evidence-based clinical decisions at the points of care
• Improve the delivery of care with real-time access to the right data
• Identify & address health problems before patients become critical

The potential benefits of such a system are limitless, extending to possibilities such as disease cure, preventable care, early detection of disease, risk assessment, and epidemic predictions.

For Payers

Insurance carriers are subject to ever-evolving regulations. And being a major expense for families, health insurance relies heavily on efficiency and client satisfaction for success. By collecting and analyzing information using a centralized dashboard, payers can:

• Identify and attract potential clients by analyzing demographics and profiles
• Evaluate hospital claims and prescription data to plan targeted campaigns for specific medical conditions
• Make adjustments to regulatory changes by embedding a health data analytics solution that seamlessly integrates with the existing security model
• Compare pricing data against performance metrics to determine the best value and lowest cost providers for specific services and procedures
• Uncover the potential for fraud by leveraging predictive analytics to forecast and notify in case of at-risk claims

Here we’ll explore some of the real-world examples of how healthcare facilities are leveraging data analytics to improve outcomes across the value-based care continuum.

Risk Scoring for Preventive Care
ML algorithms can identify patients at an increased risk of developing a life-threatening condition early on from the EHRs. This ensures that health facilities have the best chance of helping these individuals to avoid life-long issues that are costly and challenging to treat.

Optimal Use of Hospital Space
A data operating system with an analytical interface can efficiently calculate occupancy rates to avoid suboptimal use of space. An online scheduling tool with a visual map of patient schedules can also help health facilities maximize space & resource usage for increased revenue.

Predicting Operation Duration & Risk
One of the key objectives of leveraging an analytics platform is putting to use the predictive potential of machine learning. It can analyze patient health records to identify those at the risk of clinical decline and bloodstream infections. This information helps predict the duration of operations and avoid surgical room delays.

Predict Daily ER Visits
The system can source this data from the patient scheduling and billing database. The ML model can then analyze it to predict the expected emergency room visits daily with almost 80% accuracy. The extracted insights can help physicians to optimize ER operations and allocate staff in advance, thereby resulting in optimized patient care and positive results.

Create a Mortality Model for Global Pandemics
The data on population density, demographics, and comorbidities in a given region can be leveraged to create a pandemic model that reflects individual risk scores. Using this data care facilities can take precautions and mitigate the risk for older age groups. Telehealth apps can also be used to reduce the exposure of viruses for clinicians.

Reduce the Rate of Hospital Readmissions
Analytics tools can easily identify individuals with traits that have a high impact on the possibility of readmission. This data can give care providers an indication of when and where to allocate resources for follow-ups. Designing the discharge plan protocols based on these insights can prevent readmissions.

Stay Ahead of Patient Deterioration
While patients are hospitalized, practitioners cannot rule out the risk of infections, development of sepsis, and potential threats to their wellbeing. Data analytics can help physicians to be more responsive to any changes in the patient’s vitals or medical condition. Timely intervention is essential before the symptoms become evident to the naked eye. This can significantly improve treatment outcomes while avoiding sudden downturns.

Focus Area: Patient Care & Satisfaction

• How to predict relapse from the patient records that is collected from medical ailments, fitness trackers & medical devices on an ongoing basis.
• Which patients should be flagged for an impending risk associated with chronic conditions?
• How to reduce the number of preventable ER visits and readmissions for chronically ill patients?

How a Healthcare Data & Analytics System Solves These Challenges
With an end-to-end view of the patient’s medical history and existing health condition in real-time, it becomes easy to provide preventive care to achieve and sustain the patient’s health and well-being.

Focus Area: Resource Utilization

• How to optimize the use of various medical inventory used in operating rooms, out-patient facility, diagnostic rooms, emergency care, and also during physician-patient encounters
• How to schedule appointments in a manner that patient wait times are reduced
• How to leverage third-party data such as surveys to improve care delivery and patient experience

How a Healthcare Analytics Solution Addresses These Issues
It helps with optimal allocation and use of resources to maximize cost savings. Also supports efficient scheduling to reduce wait times and unnecessary delays, thereby resulting in more satisfied patients.

Emerging technologies are continuing to push the boundaries of how health analytics can be harnessed. From AI to NLP to ML, healthcare organizations are wielding these for a positive impact.

• AI can be used to analyze data for identifying malignant tumors, diagnosing disease, and structuring clinical trial cohorts, among several other uses.
• ML is essentially leveraged to generate models that describe data. As the volume of data keeps growing, the algorithms adapt to new information and create models that accurately fit the data. ML is often used in a predictive capacity for precision medicine.
• NLP uses different techniques to make sense of human-generated writing or speech especially across extracting data from medical records and sorting clinical documents.

This is just a glimpse of what the future holds for health data analytics. With technologies advancing at an exponential rate, innovations that can create an even bigger impact are inevitable.

Developing such a custom healthcare data analytics solution is an expensive and time-intensive project. But it allows for a high level of customization. So healthcare organizations must build a custom platform that caters to their unique data and analytics needs.

Here are some key factors that can affect the total cost of building a comprehensive platform.

• Analytics solutions vary drastically in deployment times depending on how the solution is integrated. Also, the sensitivity of healthcare records requires a focus on time. Define a timeline that best fits the needs of your practice.
• Will the solution work in sync with your existing security protocols? For any medical practice, data security should be a top priority. Building an inadequate platform can leave your confidential clinical data susceptible to data breaches.
• Will the solution scale to accommodate the compliance changes and data management needs as your practice grows? Every healthcare practice is subject to changes and so your platform should be scalable enough to secure the future of your practice.
• The total cost of development is also affected by the technologies used, the choice of platform and framework, the servers needed, the amount of data to be handled, third-party integrations, and more.
• The amount of training for non-technical users, technical support and maintenance required will also impact the cost.

The focus of a robust solution should be to facilitate the highest quality of care possible. It should not create any obstacles in care delivery by eating up valuable time and resources. The right solution must enable the health organizations to stay focused on doing what matters the most – taking care of patients and improving treatment outcomes.

Your technology is only as good as the data you feed into it. With Rishabh, you get access to experts who know how to use data in any form to generate result-oriented solutions.

We can help build a custom platform that is scalable, easy-to-use, secure, and built for your specific health systems. We have assisted global healthcare organizations to develop custom healthcare software solutions that simplify, scale & systematize better decisions right from data selection to evaluation and visualization.

• We don’t adopt a one-size-fits-all approach. Our team works with you at every step to develop solutions that are tailor-made to your organization and goals.
• Our scalable technology stack enables us to solve your data challenges and accelerate innovation that helps you stay ahead of the curve.
• Our skilled workforce becomes an extension of your team and helps you unlock the power of data to drive better outcomes & bring more value.
• We integrate actionable insights into your existing platforms to improve the speed and accuracy of your clinical decisions.