The US healthcare analytics market size was approximately 2.8 billion. By technology type segment, the largest market share was grabbed by predictive analytics (72%) followed by prescriptive analytics (18%) and descriptive analytics (10%) respectively

• Definition / Scope
• Market Overview
• Top Market Opportunities
• Market Trends
• Industry Challenges
• Technology Trends
• Regulatory Trends
• Market Size and Forecast
• Market Outlook
• Technology Roadmap
• Competitive Landscape
• Competitive Factors
• Key Market Players
• Strategic Conclusion
• References
• Appendix

Definition / Scope

Analytics is study of gaining insights through combination of data and application of qualitative and quantitative analysis. It generates fact-based decisions which is used for planning, management and learning purposes.

Analytics can be used across any industry or field, thus, healthcare analytics is use of data analytics in healthcare field. The overall healthcare analytics can be categorized by following: 

Types of analytics

• Descriptive: Exploration and discovery of information through data
• Predictive: Prediction of upcoming situation based on historical data
• Prescriptive: Utilization of scenarios to support decision making

Types of Data

• Web/Social Media data: Data extracted from social media such as youtube, google, facebook twitter etc.
• Sensor data: Readings from medical devices and sensors
• Biometric data: Finger prints, retinal scans, X-ray, medical images, blood pressure, pulse reading and other similar types of data
• Big Transaction data: Data extracted from Hospital bills, insurance claims and other transactions
• Human Generated data: Data from Electronic Medical Records, emails and notes

Healthcare application areas

• Clinical decision: Analytics applied to observe and communicate information about diseases
• Healthcare Administration: Analytics used to manage operations of care providing organization intended to improve quality of care and reduce operating costs.
• Privacy and Fraud detection: For privacy, protection of patient identity and using analytics to identify unauthorized activity
• Mental health:Analytical decision support for psychiatric patients or patient with mental disorder
• Public health: Analytics used in study of population health of a region or entire nation.

Market Overview

In the US healthcare, factors such as readmissions, inappropriate use of antibiotics and fraud constitute about 47% of the expenditure every year. The majority of these costs were also associated with low quality care and as a result approximately 251,454 patients die every year in the US.

Thus, the US healthcare industry has moved towards data-driven model to enable decision making based on information. A large volume of data is collected through the Healthcare IT system on regular basis from which information is extracted and support decision-making.

Overall, the transition to data analytics is to facilitate a value-based healthcare industry.

The Centers of Medicare and Medicaid Services in the US used analytics to reduce readmission rates at hospitals which helped it prevent fraudulent payment of about $115 million.

Besides this, the use of analytics is being helpful across several areas such as disease diagnosis, treatment which is leading to improvement in quality and reduction of cost. Thus, according to estimates, application of data mining can save up to $450 billion each year for the Healthcare industry in the US. 

The volume of data in healthcare is growing at an exponential rate where just 153 exabyte data was produced in 2013, the estimated 2314 exabytes will be produced in 2020. Since 2013-2020, the expected rate of increase was registered at 48% annually i.e. in 2018, approximately 1082 exabytes of data was produced. 

There are several significant investments in technology to improve the physicians’ use of data. Nearly all (96%) of the hospitals across the US have online portals dedicated to help the doctor to track testing, imaging and visits and give patients access to information without necessitating a call or fax to the doctor’s office.

As a result, at present an average physician in the US spends about 49% of his/her time accessing EMR (Electronic Medical Record) and only 33% in clinical face time. Thus, EMR technology is being utilized more often to collect data and improve patient outcomes. 

Top Market Opportunities

Within, the healthcare system in the US, health plans and health providers have approached population health differently because of the data they had access to.

For instance, the health plans typically analyzed claims and enrollment data to identify high-cost populations and incompetent care patterns at national level, looking for specific opportunities to improve quality and reduce cost. The analytical approaches of payers focused on variations in acute and chronic care practices but don’t provide insights to providers on ways to progress.

On the other hand, the claims data that the payers have access to are not real-time, in addition, they don’t have access to clinical information that is limiting their ability to make coverage decisions. Health providers in contrast rely on clinical data to deliver patient-centered care, giving personalized care, understanding patient’s health problems, medical history and gaps in care.

Thus, the clinical data is accessed from HER which support to address such issues. Then again, the information about care present outside the hospital domain is not available or timely. As a result, health systems have little or no data for healthy consumers who might access the delivery system for wellness and preventive care visits. 

These scenarios that exist at present can be considered as growing problems and fragmentation in data in healthcare, it also holds opportunity for healthcare providers and payers to work together to maximize their synergies and provide a better overall experience to the patients. For instance, if providers identify patients that are at risk for unnecessary utilization, they can develop interventions to improve care.

And in order to unlock such insights, both clinical and claims data are required. Both health plans and health systems are able to attain better outcomes and which can be gained through partnership where they build combined expertise, data and technology capabilities, and share resources.

Some of the health plans are already collaborating with providers where they are sharing data such as monthly dashboards on utilization patterns and cost drivers for the patient population which is leading to preventable admissions and ER visits.

Further, collaborating on analytics can include, sharing data inputs, leveraging analytics, and incorporating actionable reporting that enables real-time outputs.

The shared analytics is definitely a huge opportunity between health plans and health systems to improve quality, reduce cost, and succeed in the new value-based care environment. 

Market Trends

Enhanced decision making: The real time access to clinical analytics is being utilized in healthcare today which is driving the behavior through set of prescribed best practices in clinical care based on multiple conditions of patient.

Due to the EHR/PHM technologies upon which the providers are becoming increasingly dependent, the ability to pinpoint the apt treatment for patients is leading to improved decision making which is further grabbing the interests of healthcare companies to invest in data analytics competencies.

For instance, the healthcare companies today are receiving detailed information about their patient’s lifestyle such as, their change of jobs, divorce, number of events they participate in or switching their insurance payers. These information in return could give insights about the particular person’s stress levels.

Completing the dataset with view of information about their personal lives has the ability to drive better quality clinical care. Thus, access to such holistic dataset drives better care as well as improvement in predictive analytics processes. 

Holistic Patient View: The development and access of universal patient database is going to be realized soon in healthcare. Access to real-time socio-economic data is new borderline in healthcare analytics.

This data is being coupled with EHR and PHM tools that is providing an overall picture of member/patient that providers are being able to see. LexisNexis Health Care is one institute that is helping in this context as they have the public records and commercial data on members which they are linking patients to payers. Thus when clinical data and claims data are both combined, it gives the most comprehensive insight to both parties (providers and payers).

The use of socioeconomic data to help evaluate risk is emerging rapidly and is set to improve and be adopted broadly in the next three to five years. Thus, within this time period healthcare analytics will explode in the US. 

Institutional Practices: The deep rooted practices in healthcare is also creating several barriers in adoption of data analytics in the industry. Although clinical decision support is one of the most prominent areas of application in healthcare analytics, there are several challenges associated with this area.

The construction of decision support tools hasn’t actually focused on how decisions are made and workflows related to support those decisions. These data tools do not work in alignment with the decision-making structures of the institutions and are rather becoming a burden for physicians.

In some cases, these have led to high-profile mistakes, physician burnout and general dissatisfaction of the institution with the tools. As these tools lead to workflow disruption, many physicians are reluctant to adopt these new data tools. 

Misaligned data: The disintegrated landscape of the healthcare industry in the nation is one of the largest barrier to application of data analytics. The several components in the industry are focused about their own incentives rather than that of a system as a whole.

For instance, at present, care delivery institutions may not know that their patients have visited emergency rooms unless told by insurer that has claims data. Meanwhile, providers have clinical data that can be useful for insurers to manage their patients’ costs in advance.

But both claim and clinical data are non-integrated that leads to missed opportunities to leverage the data analytics to its fullest. 

Industry Challenges

Although, the healthcare analytics holds immense promise, the industry lags behind other major sectors taking advantage of big data. Most healthcare providers in the US are not taking a clear approach on incorporating data into their operations.

As a result a recent survey showed up-to 56% of hospitals have no strategy on data governance or analytics. Some of the major challenges that continue to plague the innovation of big data in the industry are follows: 

Sensitivity to care decisions: Unlike other industries, the healthcare decisions are based on hugely sensitive information which requires timely action and sometimes are riskier as it deals with life-death consequences. The data also requires to be monitored regularly and extensive staffing to collect and tabulate information.

The healthcare must also consider patient decision which could sometimes counter that of an expert recommendation, in such case, all the analytics effort goes into waste. Due to the complexity present in decision-making in healthcare both providers and patients demand high standard of data analytics tools in healthcare, this has proven very challenging for designers who make such tools.

As a result, the clinical insight software many times do not make better insights than doctors. For instance, one of the most advanced systems, IBM Watson have made series of unsafe and incorrect recommendations as they were not based on real patient data but synthetic cases.

In addition, the sensitive nature of healthcare decisions is further creating privacy issues. As there are wearables that have made health status monitoring accessible to patients, these data are not subjected to federal privacy laws, allowing companies to access information and share it with third-parties. 

Problematic data conventions: Various data models in the industry is hindering the expansive use of data analytics across the healthcare in the US. The healthcare data at present is fragmented among different entities and framework which has limited the data to create insights and a granular database.

These factors are also leading to rising costs. In addition, the healthcare industry is further reluctant to make information available to open data commons that are accessible to all and are up-to date.

The reluctance is a result of possible violation of privacy. Another data challenge is quality where at present the analysis of data is being done with mostly low quality data.

For example, Google Flu Trends, turned out to underperform far more basic models, despite analyzing far more data, because the analysts were drawing data out of misleading group i.e. Google users. 

Lack of policy changes: The Federal policy has supported the adoption of Electronic Medical Records (EMRs) but providers still struggle to share data in useful ways. Nether, care providers nor EMR vendors have incentives to share data however interoperability of EMR could lead to improved care and save costs.

The 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act further aggravated the interoperability where EMR systems were designed and integrated without the Health information exchange capabilities.

There are several Federal policies present such as, Fast Healthcare Interoperability Resources (FHIR) and Accountability Act (HIPAA) that exist to support interoperability but the movement hasn’t utilized it to allow exchanges. 

Technology Trends

Wearables technology: The wearables technology is being used to collect patient’s healthcare data. Mostly wearables such as pedometers and heart rate monitors are blowing up in the market. However, with 35 million units of sales in US, in 2018 alone, the consumer fitness brands are the most popular type of wearable.

In an industry-wide comparison wearable technology usage rate in healthcare analytics is the most in the US (up to 85%). These wearables are allowing various types of testing such as glucose tests, blood pressure and genetic testing which is allowing individuals to take control of their health without being dependent on doctors or pharmacies.

As a result, the wearable technology is also leading to rise of new business model in healthcare of the country known as direct to consumer testing market that is expected to reach $350 million by 2020.

At present, the market of wearables is controlled by the technology companies, but in near future there is huge opportunity for the healthcare organizations to offer such wearables under the HIPAA act that could produce more medically robust and relevant data. 

Telemedicine: Telemedicine or also known as the virtual delivery of healthcare is being proliferated across the industry for variety of reasons. Some of the big tech companies such as Google, Verizon and AT&T have startups in telemedicine space along with number of insurers and startups eager to invest in the technology.

The telehealth market in the US was recorded at $1.9 billion in 2018. Not only telemedicine is increasing access of healthcare to unearned communities, given the high cost of building hospitals and health centers, telemedicine is being used to mitigate some of the demands to access to physicians in rural areas with no proper health facilities.

For instance, through Stanford Medicine’s telemedicine program, called ClickWell Care, patients can choose when and where they meet with their health care providers, whether in-person, over the phone or via video conferencing.

Thus, more than 55-60% of the visits are handled virtually in healthcare at present, this is definitely going to drive data analytics as increasing amount of virtual encounter in care would help to collect data electronically with ease.

Regulatory Trends

The establishment of Affordable Care Act (ACA) or Obamacare led to legislative change in the healthcare industry of US. Since then, the country has expanded health insurance cover to almost 20 million Americans through Medicaid expansion and establishment of health information exchanges.

For 2019 and beyond, the ACA is expected to continue and one of the major focus area is going to be healthcare analytics.

Under ACA, the care delivery organizations agreed to take less payment from the Medicare and participated in programs such as shared savings program or bundled payments. These new payment structures has led them to become more data driven and incentivized them in return.

The EHR (Electronic Health Record) program introduced by the Federal government led more than 80% providers to adopt the data digitalization technology which is critical to data-driven healthcare.

However, the key issues such as lack of data professionals and cost of using analytics are some barriers that government must oversee to promote more data-driven business model in the industry in future. 

The new payment models have been established in national healthcare by the Federal government through the representation of an act called Medicare Access and CHIP Reauthorization Act of 2015 (MACRA); the game changing law that sets many elements of payment reform.

The law has been introduced to drive increased participation in risk-based care models across health plans and not just Medicare. In contrast, the traditional fee for service (FFS) payment models that existed before MACRA offered little incentives for the healthcare providers to invest in analytics.

MACRA on other hand, will provide payment updates to physician based on their past year’s performance and other alternative compensation models to providers that involve high risk, however this could spur development of analytical tools that help providers progress their overall performance.

Market Size and Forecast

• The global healthcare analytics market was recorded at $7.6 billion in 2018 and is growing at a CAGR of 28.3% in (2019-2024) period expected to reach $18.05 billion by 2024.
• As of 2018, North America is the highest growing region accounting 50% of the total global market share of healthcare analytics followed by Europe. The market share of the region is recorded at $3.13 billion as of 2018. During the same time, the US healthcare analytics market accounted for more than 90% of the regional industry share. Thus, as of 2018, the US healthcare analytics market size was approximately 2.8 billion. 
• By technology type segment, the largest market share was grabbed by predictive analytics (72%) followed by prescriptive analytics (18%) and descriptive analytics (10%) respectively. 

Market Outlook

• The global healthcare analytics market was recorded at $7.6 billion in 2018 and is expected to surpass $18.25 billion by 2025 registering a growth rate of 28.6% during the forecast period of 2019-2024. The North American market is going to register a growth of 29.54% during the same period and reach $11.4 by 2025
• And finally, the US market is expected to surpass $10.9 billion by 2025. The growth in the US healthcare analytics is particulary supported by well-established healthcare infrastructure and acceptance of advanced technologies.
• According to the region, APAC is going to receive highest growth in (2019-2024) period followed by North America. The healthcare infrastructure in the US that is undergoing a positive trend and also the presence of several globally prominent players in the country is further driving the growth of the entire region. 
• By technology type, the descriptive analytics market is expected to grow during (2019-2024) period, In the US, McKesson launched Health Mart Atlas, which is the largest network of high-performing community pharmacies dedicated to delivering high-quality care with a personal touch. In addition, the prescriptive analytics segment is anticipated to witness lucrative CAGR between 2019 and 2024 owing to its ability to predict possible outcomes and its implications on crucial business metrics. .

Technology Roadmap

In the US, over time, big data is expected to lead huge breakthrough in the healthcare industry. For instance, the patient’s medical records with be combined with data extracted from wearables and genetic testing. 

Wearables: The next generation wearables will be the ones that can be implanted into human body for instance, in form of a chip. These wearables will create streams of data and health tracking more effective.

As per the Stanford Medicine research, the wearable devices can also use biosensors to detect symptoms of possible illness such as Lyme disease. In future, the wearable might also hold technology where they have the ability to detect and even treat illness if it is in an early stage. 

Personalized medicine: The personalized medicine market in the US is expected to reach $2 billion by 2022. Along with that, the federal government is also increasing the investments in personalized medicine and genomics research.

In near future, personalized medicine is going to gain more traction than at present, doctors will be able to choose personalized medicine as a treatment option by tailoring medication to person’s unique genetic makeup.

The personalized medicine is derived by combining a person’s genetic blueprint with data of their lifestyle and environment and evaluating it alongside an array of patient blueprints to predict illness and determine the best course of treatment. 

Telemedicine: Telehealth is also anticipated find benefit of the global implementation of smartphones, with some estimates expecting half of the global population to have smartphones by 2019. The average doctor visits per person would limit to 4.1 per year in the US which is lowest in comparison to rest of the countries in the world. 

Competitive Landscape

• US is leading big-data revolution in healthcare with increased availability of information in the sector. US government and other public parties have been opening vast sources of healthcare knowledge, including data from clinical trials and information on patients covered under public insurance programs. 
• As of 2018, the Healthcare accounts 17.9% of the GDP of the country which is nearly $3.5 trillion. To discourage overutilization, the industry has taken a new approach which is data analytics. As the healthcare stakeholders receive incentives for exchanging information, this has the potential to lead to more than $300 billion of saving for the industry through various approaches in data analytics. 
• In the healthcare analytics sector within US, the market is not limited to conventional players as more than 200 small businesses are operation across several categories in analytics and have developed innovative applications. About 40% of those applications were based on direct health intervention or predictive capabilities. 
• Some applications have also led a breakthrough in the patient monitoring process. For example, a company called Asthmapolis created a GPS-enabled tracker that records inhaler usage by asthmatics. Then the information that is transported to central database is able to identify population based trends. Similarly, another company Ginger.io offers a mobile application in which patients are assisted by their providers with behavioral health therapies. The app records data from calls, texts, geographic location, and even physical movements. The application is so insightful that, for instance, lack of movement could signal that patient must be unwilling or depressed and irregular sleep patterns suggest that their anxiety attack is looming. 
• Within the US, the several parties involved in the healthcare analytics such as startups, tech companies, universities, hospitals and public institutions are collaborating to maximize the benefits of big data in health. For instance, IBM Watson and Memorial Sloan-Kettering have recently partnered to develop treatment plans for cancer patients. Also, Microsoft and the University of Pittsburgh Medical Center are uniting to improve the delivery of health care. Despite of looming challenges in healthcare analytics, these partnerships are intended to solve problems such as data governance, IT staff among others. 

Competitive Factors

• Some of the players involved in healthcare industry have already realized ample of benefits including cost savings and the examples include :
• Kaiser Permanente’s full-fledged new computer system called HealthConnect ensures data exchange across all medical facilities and promotes use of electronic health records. For instance, its integrated system led to improved treatment of cardiovascular disease which in turn saved the company $1 billion due to reduced lab tests and healthcare visits. 
• Blue Shield of California that partnered with NantHealth, is improving health care delivery across patients by integrating hospitals, doctors and health plans to deliver care that is more personalized. It’s integrated technology system helps Blue shield to improve a number of areas such as prevention and coordinated care. 
• Similarly, AstraZeneca established which established a four-year partnership with WellPoint’s data and analytics subsidiary, HealthCore, conducts real-world studies to determine treatments for some chronic diseases. The company uses HealthCore data along with its clinical-trial data to guide R&D investments. 
• The recent efforts such as Verily’s Project Baseline is being conducted to understand a broader concept of healthcare. The Alphabet’s life sciences wing, Verily is set to collect comprehensive data from 10,000 participants over four years in collaboration with Stanford and Duke School of Medicine to build a comprehensive map of human health and disease.
• Researchkit commercialized by Apple in 2015 is an open-source software framework designed for iOS devices to collect genetic data and medical test results for use in medical research and diagnostic applications. In an average, the data gathered from Apple’s vast user base creates a powerful resource for medical research.

Key Market Players

The top 10 players in the healthcare analytics in 2018 are all from US and they are follows:

IBM: It is a million dollar American company innovating solutions across industries. IBM Watson Health is the healthcare industry’s premier HIPAA-enabled, cloud-based data analytics platform. The company utilizes big data for clinical integration, predictive analytics and business intelligence.

Cerner: It is one of the top data analytics company in healthcare in the US that connects technology and people. Around the globe, 27000 contracted global healthcare providers use its solutions to improve patient-centric care. It also has its own dedicated data warehouse solution called HealtheEDWSM that discovers hidden trends and variables within healthcare data.

Health Catalyst: Headquartered in Salt Lake City, the company utilizes adaptive data architecture framework to solve problems in healthcare. Its model can be implemented at short span of time and also can be optimized for any IT environment already in place, in addition their solutions can be scalable over time.

The Health EC is private global data analytics business headquartered in New Jersey. Its solutions are transforming the healthcare environment with intuitive technology and advisory services. The company processes over 40 million claims annually while providing services for more than 425,000 patients under reimbursement models such as Medicare and Medicaid.

Epic is a renowned American healthcare software company that works in improving areas such as patient engagement to large healthcare data analytics systems. The company has data of over 190 million patients in order to help healthcare as a whole to run more efficiently. The company’s software is distributed across a wide range of sectors from hospitals and clinics to universities and research centers.

Amitech, the American healthcare data analytics company provides informed healthcare to ensure better health. The solutions provided by the company aims to transform the current market landscape of healthcare of the country into one that is sustainable and informed. It also continuously monitors the outcomes so that it can improve its solutions over time.

Acmeware is an American healthcare data analytics company that operates throughout the US and Canada, employing only 30 full-time employees and partnering with with more than 50 hospitals. The company’s product OneView is a web based application for analysis, presentation, consumption and distribution of healthcare information. the system is growing popular among providers and is beginning to incorporate additional custom and standard packages that leverage both data and analytical content.

Conifer: It offers expansive range of healthcare solution among which data analytics is one. Its own analytics platform called Conifer Core Population Health Intelligence platform offers healthcare facilities in more secure way. In addition, an interactive dashboard is included along with preconfigured reports and self-service analytics platform that can integrate and leverage data sets.

Prognos is an American healthcare Analytics Company established in 2003 and since then the company is the only player in the market providing multi-lab intelligence solutions to its clients. The company not only utilizes clinical diagnostics expertise but also AI and highly scalable technology platforms. In addition, the company also works in collaboration with one of the US’s largest labs in order to provide new sources of revenue, improve data quality and enhance clients’ value in healthcare industry

Optum has its own data analytics platform called OptumIQ that is leading analytics and applied expertise all combined into one product. It is one of the world’s leading healthcare analytics company that has over 26000 experts who work for the company to build a common system to provide purpose, guidance and results for healthcare professionals and healthcare facilities.

Strategic Conclusion

The emergence of big data in healthcare industry of US has definitely led to increased focus on collection and analysis of data from different sources for better customer service and improved quality care.

In addition, the investment in other technologies such as mHealth apps, telemedicine, and the Internet of Things (IoT) by the healthcare companies is further going to boost the data analytics as new streams of data sources open from the usage of these technologies.

US is the leader in healthcare analytics and its market share is further going to be retained due to these technological advancements facilitating information sharing and leading improved compliance.

However, gaining insights from data would require a skilled data personnel and the growing skill gap may act as a barrier to the healthcare sector from fully leveraging the analytics which is the only concern for the industry at the moment.

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Appendix

EHR/EMR- Electronic Health Record/Electronic Medical Record