Analytics use cases in healthcare
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Top 10 Healthcare Analytics Use Cases in 2024

11 Dec 2023

Data analytics in healthcare is intended to offer data-derived intelligence by acquiring, combining, and analyzing data from multiple sources. Adopting healthcare-based analytics enables healthcare organizations to improve patient care, cut costs, and streamline operations. The healthcare analytics market is set to reach $85.9 billion by 2027, growing at a CAGR of 25.7% from 2022 to 2027.[1]

Now is the time for healthcare organizations to embrace a data-driven, cost-effective, and patient-centric approach. Draw inspiration from these top 10 healthcare analytics use cases and unlock the value of your healthcare data.

Key Contents    

What is the Role of Data Analytics in Healthcare?

Advancing healthcare and improving treatment outcomes requires both measurement and extensive analysis of large healthcare data sets. Data analytics involves the process of aggregating, organizing, cleaning, processing, and analyzing information to identify hidden threats and make informed decisions.

However, the modern healthcare ecosystem is inundated with massive data generated by patient records and IoT-enabled medical equipment. This is where the strategic use of analytics in healthcare helps in achieving better treatment outcomes and operational efficiencies. Let’s look at the profound impact of evidence-based decision-making on healthcare practices and policies.

Types of Healthcare Analytics to Apply in Your Healthcare Practice

Healthcare data analytics employs different techniques to extract insights from vast datasets. Here are the 4 key types of data analytics in healthcare facilities.

4 Types of healthcare data analytics

Resource Planning with Descriptive Analytics

Descriptive analytics helps in resource planning by analyzing past patient records, identifying treatment trends, and highlighting areas that need improvement. By analyzing past flu seasons, for instance, healthcare providers can determine peak season symptom severity, and refine safety measures, such as those implemented in EHRs. This ensures efficient resource allocation and improved patient care.

Early Intervention with Predictive Analytics

Predictive healthcare analytics solutions leverage both historical and current patient data to foresee future outcomes. It enables healthcare providers to predict patient needs, identify at-risk individuals, and precisely predict potential readmissions. This proactive approach offers opportunities for early intervention, resulting in significant cost savings and improved patient outcomes.

Optimizing Patient Care with Prescriptive Analytics

Prescriptive analytics forecasts future trends using Machine Learning algorithms. It is crucial in determining the ideal course of action for optimal treatment outcomes. It guides healthcare professionals in creating effective treatment plans based on past outcomes and aids in making informed decisions for resource optimization.

Root Cause Analysis with Diagnostic Analytics

Discovery analytics employs sophisticated software and historical data to innovate healthcare solutions. This includes investigating the causes of illnesses or health trends and identifying the reasons for sudden increases in hospital admissions. It facilitates prompt intervention, preventive measures, drug development, and the proposal of alternative medical strategies for treatment.

Real-time analytics, combined with the potential of artificial intelligence (AI) is poised to disrupt the healthcare sector in the coming years. It has already triggered a wave of innovation with patient-centric KPIs driving precise clinical decisions. We can help you leverage the potential of data science, Business Intelligence, and AI to forecast health trends, make more informed decisions, and suggest effective tests, treatments, and medicines to patients.

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Healthcare Analytics Use Cases

In the dynamic landscape of healthcare, integrating data analytics emerges as a transformative force, reshaping how healthcare organizations approach disease prevention, patient care, and overall healthcare management. Let’s look at the top 10 data analytics use cases in healthcare and how they improve outcomes across the value-based care continuum.

Top 10 data analytics use cases in healthcare

1. Driving Innovation in Therapies and Disease Control

Healthcare analytics assists in discovering new therapies, innovative drugs, and technologies by identifying strengths and weaknesses in trials and processes.

2. Optimized Prescription Processes

Analytics optimizes medication management, enhances prescription quality, and identifies and remedies medical errors, ensuring patient safety.

3. Personalized Healthcare

Wearable devices, like Apple Watch and Bluetooth-powered inhalers, demonstrate how big data is revolutionizing patient-centric care by detecting irregular heart rates and reducing emergency hospital visits.

4. Early Detection and Timely Intervention

Identifying at-risk patients, predicting readmission likelihood, and anticipating staffing needs are just a glimpse of how predictive analytics transforms the patient experience, care quality, and treatment outcomes.

5. Real-time Monitoring for Enhanced Efficiency

Efficient hospital operations are achieved through operational dashboards, forecasting patient loads, monitoring real-time data, and enhancing patient care with dynamic healthcare dashboard portals.

6. Efficient Supply Chain Management

Healthcare facilities struggle to strike a balance between maintaining optimal medical supplies and overstocked inventory. Advanced analytics aids in precise inventory management, preventing shortages, minimizing waste and saving big. Analytics also helps in identifying cost-effective suppliers so healthcare organizations can negotiate contracts and streamline procurement processes – all of which is ultimately crucial for patient well-being.

7. Insurance Claims KPI Dashboards

Healthcare leadership relies on intuitive dashboards, breaking down complex data into easy, actionable insights. From flu trends to bed inventory, these dashboards empower care teams with the information they need for strategic decision-making.

8. Financial Risk Management

Healthcare organizations often face financial challenges like unpaid bills, underused records, and reduced reimbursements. Predictive analytics powered by advanced technology assist in identifying accounts with outstanding payments unpaid bills, assessing insurance coverage, and more. Data analytics services empower hospitals to recognize potential patterns of fraudulent activities, whether intentional or unintentional, and take preventive measures to enhance cash flow.

9. Reduced Inpatient Readmissions

Leveraging patients’ medical history and discharge data, hospitals identify those at risk of readmission. This information fuels the creation of targeted preventative care strategies, reducing readmission rates and enhancing patient outcomes.

10. Efficient Bed Utilization

Efficiently managing available beds remains a challenge for hospitals. Data scientists come to the rescue by predicting future bed demand and identifying when occupied beds will become available. This proactive approach streamlines bed utilization and enhances resource management.

The above healthcare analytics use cases state that data analytics in healthcare is not just a technological advancement but a revolution with the potential to create healthier societies, reduce costs, and improve patient outcomes. Embracing this transformation is imperative for healthcare systems worldwide as the future unfolds as a data-driven and patient-centric domain. The possibilities are boundless, and the journey toward a healthier world begins with the integration of data analytics in healthcare.

Unlock the Power of Healthcare Data Analytics with Rishabh Software

Your technology is only as good as the data you feed into it. With Rishabh Software, you get access to experts who are well-versed in building a connected ecosystem that generates data-driven healthcare insights so you can drive productivity and profits while delivering value-based care!

Harnessing cutting-edge technologies such as AI, ML, Data Analytics, Cloud, and IoT, we can help you build dynamic web and mobile healthcare analytics applications that are scalable, secure, and built for your specific use case. We’ve assisted global healthcare organizations with result-oriented custom healthcare software development services, enabling the best medical care.

The Success Story of a Dedicated Healthcare Dashboard Portal

A US-based healthcare organization leveraged a centralized dashboard for real-time global pandemic visualization. The outbreak of the Covid-19 pandemic compelled a healthcare organization to develop a dashboard that depicts immediately.

  • The overall spread and cases by region
  • The number of recoveries and deaths
  • The number of new cases and tests done
  • Comparison of the spread between locations

Considering the urgency of the global situation, the solution was developed with a structured approach and made live within a strict deadline. The key features of the solution included.

A statistical model to predict the pandemic end;

  • Clinical trial data
  • Social integrations
  • Fact-checker

The solution resulted in a 98% accurate data comparison of the spread & mortality rate along with granular insights into pandemic control, filtered by location. Such custom solutions are especially beneficial in a fast-paced environment where providers are handling several critical patients simultaneously, which is a situation susceptible to human errors.

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Frequently Asked Questions

Q: Why healthcare needs analytics?

A: Powered with core capabilities in technologies like artificial intelligence and machine learning, a smart analytics solution can forge seamless connections across the healthcare ecosystem. Here’s how:

  • 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
  • Improve communication and coordination among diverse healthcare stakeholders.
  • Analyze genetic profiles to identify drug targets, resulting in the development of more effective and less toxic drugs.

Q: What is the future of data analytics in health care?

A: Data analytics is revolutionizing healthcare and is poised for even greater impact. Technologies like wearable devices, gene sequencing, and artificial intelligence are ushering in an era of unprecedented data collection by healthcare organizations. This wealth of data holds tremendous potential for enhancing patient care:

  • 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.

Data analytics is pivotal in revolutionizing healthcare, enhancing patient care, and making healthcare more accessible and efficient.

Q: How are data and analytics transforming decision-making in healthcare?

A: Data and analytics are reshaping healthcare decisions across various domains:

  • Informed Patient Care Decisions: Doctors use data to compare treatment options and predict complication risks.
  • Efficiency and Cost Reduction: Healthcare organizations identify areas of waste and high-risk patients for readmission prevention.
  • Effective Public Health Interventions: Public health officials allocate resources more effectively to prevent disease spread.