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
- Healthcare providers, health systems, insurance companies, and vendors are seeking out experienced data scientists and machine learning experts with the healthcare data being unstructured and all data being difficult to access and analyze.
- According to the survey conducted by the Deloitte Center for Health Solutions, improving clinical outcomes is the top driver for big data analytics investment.
- The same survey results showed that 57% are currently examining how to use data to improve patient experiences, and 80% consider consumerism a key driver over the next three years.
- 39% of organizations had between 11 and 25 employees dedicated to data-driven projects in 2018, up from 33% who had fewer than 10 people who focused on analytics full-time in 2015.
- The focus of staff organizations hiring decisions is shifting from those building analytics infrastructures to roles related to data scientists, visualization developers, and data architects.
- 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.
- Providers see an increase in patient access (accommodation of more patients, sooner) and revenue, lower cost, increased asset utilization, and improved patient experience.
- Penn Medicine launched Artificial Intelligence that will help speed up innovations in the clinical sphere and beyond.
- Penn Medicine Institute for Biomedical Informatics released a free, open-source automated machine learning system for data analysis in 2019.
- MIT and Brown University have also developed an interactive machine learning tool that can be used by data scientists to small business owners across industries.
- Looking for professional data scientists has become one of the main challenges for the healthcare provider’s management.
- All data scientists do not have domain-specific knowledge, which is crucial in understanding how the industry works and how it’s regulated.
- Making excellent operational decisions consistently demands sophisticated data science, which is not implemented by all healthcare organizations.
- The development of approachable tools or machine learning systems takes three years or more, which can be a constraint.
- Machine learning without automation constantly requires someone to choose a specific method and manually adjust each parameter that the technology explores.
- According to Jason Moore, director of the Institute for Biomedical Informatics,“The problem with machine learning tools is that machine learning people build them, so they’re usually only usable by those with high levels of training,”
- As per IDC by the year 2018, about 30% of the healthcare providers were using cognitive analytics to analyze the patient data better.
- 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]
- The gap between mature analytics organizations and others is growing in the healthcare segment;organizations without a data-driven strategy in place are more likely to fall behind their peers in this highly competitive environment.
- Only 3% of U.S.-based data scientists are working in the healthcare/hospital industry, and the need for more trained data experts is growing.
- Though many medical research centers/universities are developing the tools, many healthcare providers are using tools developed by IT companies like AiCure, NextIT, among others.
- Similarly, IBM Watson's NLP feature is being used by the Caféwell Concierge app to analyze the health conditions and health goals of the users.
- 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.