Healthcare Analytics Market

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Healthcare Analytics: SWOT Analysis

Data science and machine learning are being optimized at the clinic staff scheduling, reducing the wait times, managing supplies and accounting, and even build efficient action programs for epidemics, such as seasonal flu outbreaks. But looking for professional data scientists with capabilities in the domain has become one of the main challenges for healthcare provider’s management.

Healthcare Analytics: SWOT Analysis

Strengths:

Weaknesses:

Opportunities:

  • Biotech, pharmaceutical, and research organizations posted a handful of openings for data scientists, while individual hospitals and physician groups accounted for half a dozen of the listings.
  • Health systems, including academic medical center associated systems, are looking to fill vacancies in data science or artificial intelligence departments in the near future.
  • Health systems are currently prioritizing analytics to support and execute their organization’s strategies. It is also being used for planning on investing more in analytics resources and capabilities in the future.
  • According to the survey conducted by Deloitte Center for Health Solutions, “All health systems should continue down the path of adopting and maturing their analytics capabilities—data and understanding data will be critical for decision-making.”
  • 29% more health systems are planning to hire data scientists.
  • Mobile apps powered by data science technologies present a significant opportunity for better diagnosis and more efficient disease monitoring in the system.
  • The number of healthcare institutions making data-driven decisions increases slowly but steadily, which can be seen from 31% of hospitals employing data science and predictive analytics to prevent hospital re admissions in 2016 up from 15% in 2015.
  • According to LinkedIn’s U.S. Emerging Jobs Report, the data science field has grown by 350% since 2012, and only 35,000 candidates have the necessary skills to fill job openings.
  • Machine learning is being leveraged by only 22 healthcare companies, as per an article by CB Insights.
  • A health data science company called Lumiata is working on developing predictive analytics tools for discovering insights and making predictions related to various healthcare aspects.
  • This highlights the opportunity for other companies to develop tools to do analytics/machine learning. [concluded from above insights]

Threats:

Research Strategy:

Research Methodology:
  • We started our search by looking for the SWOT analysis of the analytics tools/platforms market for data scientists in the healthcare field. Among the CASE STUDIES/INDUSTRY REPORTS from Deloitte Research and Markets, Dimensional Insight, MarketLine, Market Watch, MarketsandMarkets, among others, no such report was available for analytics tools/platforms market for data scientists in the healthcare field.
  • Hence, we went on to identify the Strengths, Weaknesses, Opportunities, and Threats based on their definition by LivePlan Blog i.e., Strengths and Weaknesses are internal factors to the company and things that can be controlled and can change, whereas Opportunities and Threats are external to the company and things that are not in control and that regulate the entire industry and all players.
  • We then identified the Strengths based on the above through the market size/share, trends, increasing job listings.
  • We have identified the Weakness based on the challenges, limitations, and lack of domain knowledge in the segment, among others.
  • We later identified the Opportunities and Threats based on the industry reports on regulations, laws, trends, new opportunities/possibilities for the industry, market entry information from LinkedIn’s U.S. Emerging Jobs report, Grand View Research IBIS, among others.
  • We have also included whether healthcare provider organizations are buying tools to do analytics/machine learning themselves, or if they're primarily outsourcing these tasks to big service providers under appropriate headers.
  • After carefully analyzing the above sources, we concluded that the healthcare segment is mostly outsourcing their analytics/machine learning work to tools developed by IT firms like IBM Watson or creating their tools like PennAI, MIT, and Brown.
  • Later we classified all the findings under the appropriate headers of Strengths, Weaknesses, Opportunities, and Threats in the box above.
Sources
Sources

Quotes
  • "Strengths and weaknesses are internal to your company—things that you have some control over and can change. "
  • "Opportunities and threats are external—things that are going on outside your company, in the larger market. "
Quotes
  • "Healthcare providers, payers, and vendors are all competing for a limited pool of talented data scientists to support their analytics needs."
Quotes
  • "Healthcare organizations have quickly revved up their big data analytics strategies in an effort meet the challenges of value-based care, according to a new survey from the Deloitte Center for Health Solutions."
Quotes
  • "The health care industry is counting on analytics to unlock value from evolving and new data sources. "
Quotes
  • "The process is highly encouraged: a record sum of $3.5 billion was invested in 188 digital health companies in the first half of 2017. Yet, the key to the meaningful industry transformation lies in the use of data science for healthcare."
  • "With about 1.2 billion clinical documents being produced in the United States annually, life scientists and doctors have a sea of data to base their research upon."
Quotes
  • "Improving hospital operational efficiency through data science boils down to applying predictive analytics to improve planning and execution of key care-delivery processes, chief among them resource utilization (including infusion chairs, operating rooms, imaging equipment, and inpatient beds), staff schedules, and patient admittance and discharge. "
  • "When this is done right, providers see an increase in patient access (accommodation of more patients, sooner) and revenue, lower cost, increased asset utilization, and an improved patient experience. "
Quotes
  • "PennAI is an automated machine learning system, the artificial intelligence engine behind it can work through different analyses with different variables and methods on its own, without human input."
Quotes
  • "Interactive data analytics tools are becoming increasingly common in the healthcare space and beyond. A team from Penn Medicine recently launched a self-service machine learning tool that allows anyone to evaluate datasets and uncover new insights."