The healthcare analytics market is projected for massive growth, forecast to reach $50 billion by 2024 according to BIS Research and $68 billion by 2027 according to Grand View Research. This data-driven transformation aims to improve efficiency, quality of care, patient satisfaction, and research insights across the healthcare ecosystem.
Here are the top 10 ways healthcare organizations of all types are leveraging analytics to innovate and enhance outcomes:
1. Detecting Fraud, Waste and Abuse
Fraud, waste and abuse siphon $100-$300 billion from the healthcare industry annually based on FBI and National Healthcare Anti-Fraud Association estimates. By analyzing claims patterns, utilization outliers, billing anomalies, and other red flags, analytics solutions can detect many forms of malfeasance:
- Upcoded or excessive services
- Kickbacks and self-referrals
- Identity theft
- Phantom billing for unperformed services
- Duplicate claims submission
- Prescription diversion
For example, Optum utilized analytics to uncover improper opioid prescribing and billing practices at a pain clinic, identifying $1 million in fraudulent claims. Continuous monitoring with analytics prevents losses and ensures appropriate access to limited healthcare resources.
2. Optimizing Operations
Operations analytics is saving healthcare organizations millions in costs while improving quality. By monitoring patient volume trends, providers can optimize appointment scheduling. Analyzing admission flows allows better bed management and ER throughput. Studying length of stay, readmissions, and care variation allows data-driven process improvements.
Cleveland Clinic decreased patient wait times by 33% using analytics modeling to improve OR utilization and scheduling. Mercy Health of Philadelphia avoids $6 million in readmissions penalties annually through analytics-driven programs. The operational efficiency opportunities are vast.
3. Clinical Decision Support
Analytics embedded directly in EHR workflow offers tremendous potential to enhance patient care at the point of need:
- Alerts for critical lab values, drug interactions, and gaps in care
- Diagnostic suggestions based on symptoms/history
- Treatment options tailored to the patient
- Predictive tools to reduce readmissions and complications
Partners Healthcare saw sepsis mortality drop 45% after implementing clinical decision support analytics. Analytics augments clinician expertise to drive optimal evidence-based care.
4. Risk Management
By analyzing past claims, diagnoses, demographics and costs, healthcare organizations can better identify and segment high-risk patient populations. Analytics quantifies who is most likely to develop complications, have high utilization, or incur excessive costs.
Targeted outreach and proactive interventions for these cohorts helps minimize risk through education, improved treatment compliance, preventive care, lifestyle changes and care coordination. Analytics converts data into actionable strategies.
5. Population Health Management
Analytics is foundational for population health initiatives seeking to improve outcomes across entire member groups. Key focus areas include:
- Care gap identification – Finding deficiencies in recommended care
- Patient segmentation – Grouping individuals by risk factors or diseases
- Social determinants of health – Assessing financial, access and other challenges
- Preventive health – Proactively managing wellness and chronic diseases
- High-risk mitigation – Reducing utilization for high-need, high-cost cohorts
Population health analytics provides the cross-patient insights needed for value-based care and risk-sharing contracts with payers.
6. Patient Engagement
Increasingly, healthcare analytics guides outreach efforts to keep people healthier through education, preventive screenings, medication compliance, health habits, and care access. For example, analytics helps:
- Identify patients overdue for services
- Assess lifestyle risks and care gaps
- Inform the right communication channel/timing
- Personalize message content
- Measure campaign impact
The London Health Commission attributes analytics-driven patient engagement initiatives for reducing emergency admissions by 15% over 5 years.
7. Personalized Medicine
Linking clinical data with genomic profiles helps better assess predispositions, disease risks, prognosis, treatment options and more — realizing the promise of precision medicine. Analytics enables:
- Risk modeling based on gene variants
- Assessing drug interactions with patient genotype
- Tumor mutation analysis guiding cancer therapy
- Modeling chemotherapy side effect likelihood
The global personalized medicine market is predicted to reach $2.44 trillion by 2028 supported by analytics unlocking the power of genomic data.
8. Predictive Modeling
Sophisticated algorithms analyze aggregated data to identify individuals at risk for adverse events from hospital readmission to sepsis and Alzheimer’s disease progression. Models augment clinician intuition with data. Each 1% reduction in hospital readmissions saves approximately $300 million annually.
9. Improving Diagnosis
Clinical analytics at the point of care enhances diagnostic decision making by:
- Alerting physicians to potential diagnosis errors and biases
- Providing risk scores for various diagnostic possibilities
- Delivering relevant patient data, guidelines and decision aids
- Identifying gaps in testing, care and information
Analytics-assisted diagnosis helps reduce the 12 million adult misdiagnoses in the U.S. annually which lead to patient harm, malpractice claims and wasted healthcare spending.
10. Operational Analytics
Real-time data streams from devices and systems fuel operational analytics for rapid insights, such as:
- Monitoring telemetry from pacemakers and smart insulin pumps
- Processing signals for early sepsis detection
- Identifying subtle EKG changes predictive of sudden cardiac death
- Assessing medication administration irregularities
- Tracking assets and medical equipment location/utilization
Operational analytics harnesses data in motion for situational awareness and timely intervention.
The top challenges impeding widespread healthcare analytics adoption include:
Data Integration Issues
Healthcare data is notoriously fragmented across disconnected systems and siloed repositories. Analytics relies on a unified, holistic view of the patient journey. But closed legacy systems, prohibitive costs, lack of interoperability standards and privacy restrictions obstruct integration initiatives.
Data Quality Concerns
“Garbage in, garbage out” plagues analytics efficacy. Dirty data with gaps, errors, inconsistencies and duplications produce flawed insights. Yet few organizations dedicate sufficient resources for robust data governance, pipelines, warehousing and stewardship – a huge oversight.
Organizational Resistance
Clinicians are often reluctant to embrace analytics fueled recommendations that conflict with their hands-on experience and judgment. Changing this mindset requires building trust in data, proof of efficacy and astute change management skills.
Interpreting Analytics
Deriving sound conclusions from analytical models – and knowing how and when to apply the insights – requires both computational and clinical acumen. Statistical associations don’t always reveal causal underpinnings. Augmenting human intelligence with analytics is an iterative learning process.
Proving ROI
The business case for analytics includes both hard benefits (cost reduction, revenue gains) and soft benefits (quality, satisfaction, safety). Soft benefits are real but difficult to quantify, making return on investment hard to pin down. But every journey begins with measurable steps.
Privacy & Security Requirements
Health data requires robust privacy and security controls including access limits, encryption, multifactor authentication, consent management and more. This is mandatory but complicates appropriate data use for analytics. The right balance enables trust and analytical innovation.
Skillset Shortages
The scarcity of analytical talent able to wrangle healthcare data is delaying progress. Data engineers, data scientists and chief analytics officers with both healthcare domain expertise and computational skills are in extreme short supply. But university programs are rapidly expanding to meet demand.
In 2023 and beyond, healthcare analytics adoption will continue growing through deliberate, focused implementations that respect the complexity of the data landscape. With a pragmatic approach, health organizations of all sizes can harness analytics to enhance diagnosis, operations, resource allocation, cost controls and ultimately, patient wellbeing. Small gains accumulate into tremendous impact over time.