What are Healthcare Analytics? Full Guide

As healthcare moves towards value-based care, there’s greater emphasis on using data to drive better and more efficient results.

Healthcare analytics help achieve this. It involves gathering and analyzing information to gain insights to improve operational and patient care decisions.

Healthcare analysts work with data from:

• Electronic medical records
• Insurance claims
• Clinical trial research
• Patient monitoring devices

Statistical techniques and dedicated solutions enable them to make sense of vast and complex medical information.

Their analysis explores questions like:

• What drives the highest readmission rates?

• What treatments lead to the best patient outcomes?

• Which appointments have the longest average wait times?

• What devices do hospital departments use most?

These queries help healthcare administrators find cost-saving opportunities, clinicians improve the quality of their care, and patients better manage their health.

On the operational side, data analytics helps hospital admins to:

• Manage staffing levels
• Utilize assets
• Control patient flow
• Optimize supply chain logistics
• Identify workflow inefficiencies
• Determine prescribing patterns

Analytics enables data-driven decision making around process upgrades, enabling everything to run more efficiently.

Clinically, medical analytics boosts results through:

• Population health analysis
• Refined diagnostic accuracy
• Precision medicine advancements
• Personalized patient care
• Risk identification
• Health profile matching

Exploring medical information gives clinicians a complete view of their patients, helping them offer more effective treatments.

Financially, healthcare leaders use analytics to fuel::

• Cost-saving initiatives
• Pricing optimizations
• Quality-based compensation models
• Fraud prevention
• Inefficiency spotting
• Claims analysis

Analytics for healthcare touch virtually every part of the industry, making it an indispensable tool for positive transformation.

Data analytics is used in various ways across modern healthcare organizations.

Clinical decision support

Healthcare analytics is often built directly into medical devices and electronic records systems to provide clinicians with real-time decision support.

You can collate patient histories, clinician notes, test results, and database medical research to generate diagnoses, treatments, and outcomes. This applies both at the point of care—like artificial intelligence (AI( assistance during surgery—or over time through population analysis.

Personalized patient engagement

Patient engagement data and AI chat and voice assistants enable organizations to create personalized health plans.

Analytics produces tailored guidance, appointment reminders, medication prompts, wellness advice, and even education for chronic conditions.

Operational workflow optimization

Detailed data around supply chain consumption, asset use, patient scheduling, and care routes enables analytics systems to spot inefficiencies and suggest refinements.

IoT sensor data can track device usage, while supply chain analytics help adjust inventory orders. Patient flow data can inform models that reconfigure care pathways and staffing priorities.

Fraud prevention

Healthcare payers use data analytics to detect unusual billing patterns, claims activity, excessive payouts, coverage exploits by providers and patients, and other fraud signals.

Predictive modeling and anomaly detection can help highlight the biggest offenders for further investigation.

Here’s how you might apply the insights from healthcare analytics in real-world scenarios.

• Diabetes intervention: Population health analysts could use an algorithm to sort through electronic medical records and claims information to find patients at higher risk for diabetes. This would enable providers to take proactive, preventative action for these groups through regular screening and early diet and exercise interventions.

• Patient bed planning: A hospital admin could input medical device data into a bed allocation model to optimize room assignments. This would enable data-driven bed planning based on expected care needs and availability throughout the day as patients check in or out.

• Spotting unnecessary actions: Revenue cycle analysts might use a claims analysis dashboard to flag high-cost procedures, tests, and referrals. This detects unnecessary decisions and encourages value-based, efficient care.

There are several ways using data analytics positively impacts healthcare outcomes across patient results, operations, and costs.

Let’s unpack some of the benefits in more detail.

Improved patient care and outcomes

The healthcare system is all about improving community health and patient care quality. Data analytics supports that mission in areas like:

• Population health analysis: Algorithms and mapping help determine health screening initiatives, community education, and local wellness programs.

• Precision medicine: Predictive analytics in healthcare help doctors select the best health plans to match each patient’s profile.

• Preventative care: Exploring patient history and predictive techniques highlight patients prone to risks and disease so preventive care can start earlier.

Better operational efficiency

Analytics optimize how and where you use operational resources:

• Asset planning: Medical device usage and patient flow analytics enhance room and staff planning.

• Workflow improvements: IoT sensors and simulations spotlight redundancies and waste to guide efficient care delivery.

• Inventory management: Predictive supply chain analytics and machine learning (ML) analysis reduce overstock while avoiding inventory outages.

Cost reduction

Minimizing healthcare waste is a vital part of value-based care. Analytics fosters smarter spending through:

• Claims analysis: Dashboards tracking care decisions and patterns help reduce pointless or duplicate testing and procedures.

• Revenue cycle management: Automated analytics provides insights for faster reimbursements.

• Fraud detection: Detecting strange billing activity prevents unnecessary spending.

Data-driven clinical choices

Embedding analytics directly into healthcare solutions offers data-backed recommendations like:

• Test and treatment guidance: Clinical dashboards provide real-time, tailored suggestions based on a patient’s entire history and data profile.

• Diagnostic accuracy: Machine learning (ML) pattern recognition enables clinicians to detect complex conditions more effectively.

• Protocol and pathway improvement: A retrospective analysis of care decisions and patient results leads to standardized improvements.

Patient communication

Data also personalizes communications and wellness plans to keep patients informed and invested in improving their health using:

• Health plans: Analytics generates customized health plans based on risk factors and patient goals.

• Digital guidance: Chatbots use patient data to offer tips, answer questions, set reminders, and encourage healthy routines.

• Portal dashboards: Data visualizations help patients digest health markers to motivate engagements.

While it has many benefits, there are various challenges involved with properly using healthcare analytics related to the data itself, regulations, ethics, and patient buy-in.

Data security and privacy

Healthcare data is incredibly sensitive, requiring analytical implementations that consider:

• Access controls: Strict user permissions and audit logs protect data from unauthorized access.

• De-identification: Masking or falsifying historical patient data before using it for retrospective analysis.

• Encryption: Secure data transmission protocols must go through analytical pipelines.

Complex regulatory environment

You’ll need to comply with the restrictions and auditing requirements of several healthcare regulations, including:

• HIPAA compliance: The Healthcare Insurance Portability and Accountability Act (HIPAA) regulates the use of health information.

• FDA regulations: Food and Drug Administration (FDA) bodies must be able to validate the accuracy and accountability of analytics that impact clinical decisions.

Ethical considerations

With analytics influencing patient care, ethical monitoring is crucial for aspects like:

• Algorithmic bias: Analytical models that affect diagnoses must be validated across representative demographic groups to prevent skewed results.

• Patient privacy: There are limits to what types of analyses you can use individual health data for, weighed against the potential societal benefits.

Data volume and variety

The large amount and complexity of healthcare data comes with hurdles.

Essential things to address include:

• Ingestion and warehousing: Creating a scalable, efficient data lake and warehouse is crucial for healthcare data analytics.

• Uniformity: Disjointed data standards across systems mean information often requires translation into one format.

• Feature engineering: Developing useable datasets for advanced analytical modeling involves several data-wrangling efforts.

Change management

Making clinical workflows and operations more analytics-driven requires you to overcome resistance, using tactics like:

• Clinical champion initiatives: Identify an influential provider to spotlight how analytical insights have improved care.

• Operational pilots: Pilot analytics initiatives to a smaller group to demonstrate its value before a broader rollout.

• Executive buy-in: Educate leadership on the analytics’s benefits to secure adoption and change readiness.

Using healthcare analytics software is a sure way to unlock the power of your medical data.

Amplitude empowers you with a data platform designed to address the healthcare industry’s analytics obstacles.

Benefit from:

• An enterprise-grade, cloud-based data warehouse that’s tailor-made for healthcare data needs.

• The ability to combine siloed data for cross-functional analysis, with out-of-the-box security, encryption, access controls, and regulatory compliance.

• Scalable infrastructure to support large volumes of medical data and many users, even as you grow.

• Contextual data integration from all consumer touchpoints, including web, mobile, IoT devices, in-clinic systems, contact centers, and more, powering holistic analysis.

• Pre-built dashboards, KPIs, behavioral cohorts, anomaly detection, and predictive capabilities tuned to the analytics requirements of healthcare companies.

Whether you’re a hospital, insurance provider, pharmaceutical brand, medical device manufacturer, healthcare tech startup, or something else entirely, Amplitude can help you access reliable healthcare analytics for endless possibilities.

Ready to get started? Sign up for Amplitude today.