This article is one in a series covering healthcare big data and analytics, a sub-sector of both investment banking software and healthcare analytics industry performance.
Definition
Data Analytics enhances the entire process of data collection, storage, mining, analysis, and visualization to discover meaningful patterns in data. Healthcare Analytics, a subset within the broader Data Analytics industry, facilitates improved decision making by combining big data and mathematics to change the way healthcare companies think, interpret, and act on information.
As a result, firms operating within the industry hope to improve patient care, operating performance, and the bottom line by converting clinical and financial data into actionable insights.
Statistics
Between 1960–2016, National Health Expenditures as a percent of GDP rose from 5% to 17.9%; per-capita health expenditures rose from $146 in 1960 to $10,348 in 2016. During the same period, the United States population ages 65 and above represented an ever-growing proportion of the overall population. These major market forces — rising healthcare costs and an aging population suffering from chronic conditions — have brought with them an increase in federal healthcare mandates.
As such, payers and providers are increasingly motivated to invest in connected healthcare technology in order to deliver patient care more efficiently.
Why Healthcare Analytics Attracts M&A and Capital Markets Activity
The combination of persistent cost pressure, regulatory mandates, and a large fragmented vendor landscape makes healthcare analytics an active sector for strategic acquisitions and growth-equity investment. Buyers — both strategic health systems and private equity sponsors — are attracted to companies with defensible data assets, sticky SaaS revenue models, and proven reductions in administrative cost or readmission rates. For advisors and deal teams working in this space, a command of sector-specific dynamics is essential when preparing investor materials that accurately position a target’s competitive moat.
Market Segmentation
The Healthcare Analytics industry can be categorized according to technology (descriptive, diagnostic, prescriptive, predictive analytics), end-user (consumers, payers, providers, and vendors), application (research, clinical, operational, financial), and mode of delivery (on-site, web, or cloud-based).
While most of these categories are self-explanatory, it may help to define the end-user constituents:
- Consumers: Individuals who receive medical care
- Payers: Institutions, such as employers or insurance carriers, that pay the providers for delivering medical care to patients
- Providers: Institutions that provide medical care to patients
- Vendors: Firms that sell products, devices, services, and solutions to providers
Main Activities by Application
Research
The ultimate goal of healthcare research is to bridge data collection with data analysis to identify the root causes of disease as well as help evaluate disease and treatment patterns. Recent technological developments such as wearable sensors and mobile phone apps aid firms in collecting and analyzing real-time data with the intent to create algorithms which help detect and treat diseases.
Clinical Data
The creation of an electronic health records (EHR) system and a health information exchange (HIE) allows consumers and providers to standardize and securely access or share a patient’s medical information electronically, effectively reducing costs while simultaneously improving the speed and quality of care.
Operational
A 2006 study by the American Hospital Association concluded that over $30 billion was spent on unnecessary hospital admissions, and that 20% of all hospital admissions occur within 30 days of a previous discharge. Operational Analytics helps improve healthcare operations to reduce readmission rates and improve patient outcomes.
Financial
The collection, storage, and analysis of financial data can help healthcare providers optimize business performance and provide insight into the whole revenue cycle. For customers, these insights can be used to create price benchmarks to compare providers. For providers, analytics will improve billing and reimbursement processes, clinical services, payer relations, charge monitoring, and more.
Competitive Dynamics and Deal Considerations
Healthcare analytics transactions carry several diligence dimensions that are distinct from general software deals. Regulatory compliance — particularly around HIPAA and interoperability mandates — is a core diligence workstream. Data licensing agreements and de-identification protocols must be reviewed for legal adequacy. Customer concentration among health systems can create revenue-quality issues that surface in a quality-of-earnings review.
At the same time, the sector’s fragmentation creates genuine consolidation opportunities. Acquirers with existing clinical or administrative data networks can unlock cross-sell revenue and network effects by adding analytics capabilities through acquisition. Deal teams approaching a healthcare analytics transaction should plan for a detailed diligence process that covers both the software-specific items (source code, third-party IP, license agreements) and the healthcare-specific items (HIPAA compliance, data-sharing agreements, provider contract terms). The virtual data rooms industry overview provides useful context on how sensitive healthcare data is typically managed in a deal process.
Similar Industries
Business Analytics, Business Intelligence, Big Data, Cloud Computing Software, ERM Software, Computer Software, Data Management.
Professionals seeking to understand broader capital-markets positioning for this sector can also explore private capital market dynamics, which describes how growth-equity and buyout sponsors evaluate software and technology businesses in fragmented verticals like healthcare analytics.
Sources
- National Health Expenditure Data, Centers for Medicare & Medicaid Services (2017), https://www.cms.gov/.
- Population Ages 65 and Above for the United States, FRED (2017), https://fred.stlouisfed.org/series/SPPOP65UPTOZSUSA/
- Healthcare Analytics Global Market, MarketResearch.com (2014), https://www.marketresearch.com/.
- Health Information Exchange, HealthIT.gov, https://www.healthit.gov/HIE/.
- Big Data Analytics for Healthcare, Society for Industrial and Applied Mathematics (2013), https://www.siam.org/meetings/sdm13/sun.pdf/.
Britton Sorensen contributed to this article.
Frequently Asked Questions
What distinguishes healthcare analytics from general business analytics?
Healthcare analytics operates under a distinct regulatory framework — primarily HIPAA and emerging interoperability rules — that governs how patient data can be collected, stored, shared, and analyzed. Beyond regulation, healthcare analytics vendors must navigate the specific workflows of payers, providers, and government programs, each with their own data standards and integration requirements. This specialization creates both barriers to entry and defensible market positions for incumbents.
Which sub-segments of healthcare analytics are most active in M&A?
Historically, financial analytics (revenue cycle management and billing optimization) and clinical analytics (EHR-adjacent tools and predictive readmission models) have attracted the most transaction volume. As value-based care models expand, tools that link operational data with clinical outcomes — sometimes called “population health management” platforms — have become an increasingly active acquisition target.
What valuation multiples are typical in healthcare analytics transactions?
This article does not cite specific current multiples, as transaction valuations vary considerably by company-specific factors including growth rate, net revenue retention, regulatory posture, and customer mix. Practitioners should consult current market data and comparable transaction analyses when evaluating any specific opportunity. An investment banking advisor with sector expertise can provide relevant comparable company and transaction benchmarks.
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