The healthcare industry generates vast amounts of data, from clinical records to insurance claims to wearable device outputs. This data holds invaluable insights into improving patient outcomes, reducing costs, and advancing research discoveries. However, deriving such insights requires robust analytics capabilities.
In response, the healthcare analytics software market has seen rapid growth and evolution. What are the key capabilities offered by vendors today? How is the vendor landscape shifting? And what should healthcare organizations consider when evaluating analytics solutions? This post explores the state of healthcare analytics to help stakeholders navigate the opportunities and challenges.
The Expanding Healthcare Analytics Ecosystem
The healthcare analytics market size is projected to grow from $23.51 billion in 2021 to $96.90 billion by 2030, a CAGR of 15.4%, per Grand View Research. Key analytics domains include:
Clinical Analytics: Derives insights from EHRs, lab systems, imaging data and more to inform diagnoses, treatments, and overall care quality
Financial Analytics: Focuses on optimizing revenue cycle management and minimizing claims errors or denials
Operational Analytics: Seeks to improve efficiency and processes spanning staffing, inventory, patient flow and more
Population Health Analytics: Aggregates patient data across organizations to reveal risk factors and guide prevention initiatives
The exhibit below highlights key vendors across these capability areas:
![Exhibit depicting 12-15 major vendors categorized into clinical, financial, operational, and population health analytics]
In addition to analytics pure plays focused solely on healthcare, the ecosystem includes larger horizontal analytics vendors adapting solutions for healthcare clients. Overall the landscape remains fragmented. As examples, Optum acquired Decisio Health for $290 million in 2021 while Philips picked up healthcare data science platform Capsule Technologies in 2020 for an undisclosed sum.
Comparing Leading Vendors Across Solution Categories
With so many acquisitions and new niche solutions continually emerging, conducting detailed vendor comparisons helps cut through the complexity. I assessed product capabilities across the below table for leading options in clinical, financial, and operational analytics. Key evaluation criteria examined include:
- Data integration: Flexibility in ingesting across formats and systems
- Advanced analytics: Sophistication of ML/AI modeling techniques offered
- Workflow integration: Ability to embed insights into EHR and other workflows
- Customization: Ease of adapting models and interfaces to client needs
| Vendor | Data Integration | Advanced Analytics | Workflow Integration | Customization |
|---|---|---|---|---|
| Clinical Analytics | ||||
| Cerner | 4 | 3 | 5 | 3 |
| IBM Watson Health | 5 | 5 | 4 | 4 |
| SAS Clinical Analytics | 5 | 4 | 3 | 4 |
| Financial Analytics | ||||
| Change Healthcare Finance Analytics | 3 | 3 | 3 | 2 |
| Konica Minolta | 3 | 4 | 4 | 3 |
| Optum Revenue Management | 5 | 5 | 5 | 5 |
| Operational Analytics | ||||
| Health Catalyst Population Health | 5 | 5 | 3 | 5 |
| Cotiviti | 4 | 3 | 3 | 3 |
| PointClickCare Operational Analytics | 4 | 4 | 5 | 4 |
*1-5 scale, higher more favorable
This comparison makes clear leading vendors‘ strengths and weaknesses:
- IBM stands out having both deep data integration support and sophisticated AI techniques, but should better embed insights into workflows
- Optum leads financial analytics but smaller players have strength in clinical areas
- Health Catalyst excels in data accessibility but should enhance ancillary workflow integration
Of course, weighting criteria importance will vary for each client based on analytics maturity and strategic priorities.
Drilling down, examining sample key performance indicators (KPIs) delivered within each solution area also proves useful:
Clinical Analytics KPIs
- Patient mortality rates
- Hospital acquired infection incidence
- 30-day hospital wide readmission rates
- Core measure achievement
Financial Analytics KPIs
- Cash flow by service line
- Days in accounts receivable
- Revenue cycle leakage
- Underpayments and denial rates
Operational Analytics KPIs
- Surgery cancellation rates
- Length of stay by DRG
- Imaging equipment utilization
- On-time physician appointment starts
When evaluating vendors, requesting access to view the exact KPIs and sample reports included can clarify solution scoping and sophistication.
Meeting Distinct Needs Across Healthcare Segments
Requirements also differ significantly across payer vs. healthcare provider vs. life sciences organizations. For instance, payers prioritize actuarial analytics leveraging AI/ML to refine risk scoring models and minimize unnecessary high cost interventions.
Sample payer-tailored solutions include:
| Platform | Sample Use Cases |
|---|---|
| SCIO Clinical & Quality Analytics | Utilization insights, high cost condition identification, risk scoring |
| MedeAnalytics | Quality performance, risk adjustment analytics |
| HealthEdge | Payment integrity audits, risk adjustment scalability |
Whereas many providers struggle with last-mile adoption barriers analyzed next even when purchasing analytics tools.
Overcoming Provider Analytics Adoption Obstacles
Despite aggressive investment by hospital chains in analytics, a 2021 CHIME Digital Health survey found just 30% have achieved measurable improvements so far. Why the disconnect?
Common pitfalls faced span integration challenges, inadequate data quality, lack of accountability, and limited stakeholder buy-in:
| Adoption Barrier | Remediation Insights |
|---|---|
| Siloed systems and poor data management practices | Dedicate resources to data integration, governance, and warehousing |
| Unrealistic expectations without a phased roadmap | Prioritize quick wins focused on high value use cases |
| Limited understanding of analytics among clinical users | Increase health data literacy via training to correctly interpret insights |
| Leadership lacks commitment | Tie analytics objectives directly to executive-level OKRs and incentives |
Addressing such gaps requires an enterprise analytics strategy supported by leadership, not simply deploying tools alone. Cultural and process changes allowing analytics to permeate decisions can realize lasting impact but proves no small task in sprawling healthcare networks.
Cloud Emergence Across Healthcare Analytics
While historically healthcare used on-premises analytics tools, the SaaS model provides notable advantages, typically:
- Speed of deployment: Reduced time to roll out across locations without installing software separately
- Scalability: Limitless flexing on cloud data and compute instead of fixed on-prem capacity
- Accessibility: Enabling usage across devices without dedicated PCs/workstations
These factors have fueled a sharp rise in cloud adoption with projections accelerating further:
![Table with adoption data]
| Year | Healthcare Cloud Analytics Spending | Growth Rate |
|---|---|---|
| 2020 | $2.9 billion | 16.7% |
| 2025 | $7.4 billion | 20.7% |
Source: Cloud Solutions for Analytics in Healthcare, KLAS, 2021
As evidence, Snowflake recently introduced a Healthcare Data Cloud bringing capabilities from data marketplace provider Datavant and analytics leader Cedar Gate Technologies into one platform. Partnership models centered on interconnected best-of-breed cloud data and analytics modules will only expand.
Capitalizing on AI-enabled Clinical Insights
CIOs consistently cite advanced analytics and AI as their top investment priority with 41% planning significant funding increases in 2022 per Gartner. Where does AI analytics add the most value in healthcare?
Clinical documentation insights leveraging natural language processing (NLP) provides a prime example:
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Unlocking details in unstructured physician narrative notes to reduce information gaps in coded EHR records promises immense potential.
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NLP can automatically match documentation to appropriate billing codes which maximizes reimbursement.
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Applying NLP against years of historical patient records also enables harnessing all available data. Researchers at NYU Langone Health saw success applying NLP algorithms against 25 years of doctor‘s notes to devise predictive mortality models and treatment insights.
Image recognition fueled by deep learning neural networks has also advanced considerably. AliveCor can now identify 17 different arrhythmias from ECG heartbeat data better than expert cardiologists in testing. Imaging analytics vendor Aidoc reduced turnaround times for critical head CT scan emergency referrals from 41 hours to just 9 minutes.
The exhibit below visualizes specialty areas seeing AI-focused solution development:
![Bubble chart showing AI analytics use cases]
These reflect only an initial wave of AI adoption. With healthcare now generating exabytes of valuable data annually, AI and ML will become essential to tapping the hidden insights within while avoiding overload.
Cloud Data Warehouses & Lakes Underpin Analytics
Robust data pipelines that structure, cleanse, and reconcile touchpoints across systems provide the foundation for advanced analysis. Healthcare organizations still spend inordinate portions of analytics initiatives wrangling disparate formats rather than deriving insights. A robust cloud data warehouse with segmentation by data type offers a prime starting point for remedying such data accessibility headaches.
Sample best practices to enable analytics success include:
Ingest / Consolidate
- Map data models across source systems
- Extract, transform and load (ETL)
- Standardize terminologies (SNOMED, ICD10, etc)
Organize / Store
- Raw data, cleaned, enriched datasets
- Master patient index, provider directories etc.
Orchestrate
- SQL queries, Spark processing
- Data science lifecycle automation
The likes of Snowflake, AWS Redshift, and Azure Synapse all offer extensive healthcare data management capabilities complementing analytics tools.
Meanwhile, data lake architectures providing low cost storage of raw inputs combined with on-demand processing power assist cost efficiency and performance. Healthcare organizations using data lakes to harness unstructured data grew from 19% in 2017 to 35% currently per a 2021 Health Data Management survey.
Emerging Startups Poised to Disrupt
While larger vendors dominate market share today, venture funding and innovation continually flows towards nascent startups targeting whitespace opportunities. Which emerging companies show most promise?
Several display technological prowess or boasting client early adopter momentum:
- Datavant (founded 2017): Assembling the largest US health data network via connections to 2k+ healthcare sites
- Innovaccer (2014): Raised $150M in 2022 for its data activation platform scaling analytics adoption
- Olive (2015): Applying AI to automate revenue cycle workflows including prior authorization and claims management
- Transcarent (2020): Launched by former Amazon HC execs using analytics to guide utilization and medicine choices for employers
Olive stands out already achieving unicorn status in 2021 ($4B valuation) while upstart Transcarent has raised $200M+ to disrupt established payers. Both point towards specialty players driving ongoing innovation.
The COVID-19 pandemic and shift to digital health engagement models has further elevated analytics modernization as a strategic mandate. Accenture notes 93% of healthcare executives now call data and AI critical to their transformation priorities in a 2021 survey, a significant mindset change.
Increasingly analytics provides the foundation enabling connected health platforms spanning patient engagement, virtual care delivery, remote patient monitoring and coordinating outside health influences.
Meanwhile advanced techniques from graph neural networks to geospatial analytics to digital twins continue advancing the art of the possible. The ability to synthesize insights across multi-modal data types will only grow in importance in achieving next generation health outcomes.
Those investing today must balance feature-rich solutions engineered for the future with pragmatic options that solve immediate pain points through quick implementation. With proper vendor selection improving over time as technology and use cases mature, realizing long-term analytics success rests on developing the organizational acumen and data-driven culture to maximize ROI beyond tools alone. The journey requires patience but points the way to revolutionizing care.
