8 Major Challenges in Healthcare Industry
Health care industry collects a lot of information from various places (Labs, Clinic etc) but it lacks a single source of truth about the customers, As result the amount of data that is collected is not effectively used to provide valuable insights.
You would have heard it time and again that analytics can help you increase the performance, it can reduce the manufacturing cost etc. As you would know health care is still not able to leverage the power of analytics to the maximum. Before we deep dive on leveraging analytics and the application of analytics in healthcare in the upcoming blogs, in this blog we will learn what are the challenges healthcare industry is facing.
Global health care spending continues to increase dramatically at an annual rate of 5.4 percent between 2017-2022, from USD $7.724 trillion to USD $10.059 trillion.
The United States could save $175 billion in healthcare costs by halving administrative costs.
In 1960 the American healthcare industry was worth $24.7 billion and now it is $3054 trillion.
There are various challenges healthcare industry is facing, below are some of the major challenges.
Electronic health record is the collection of patient health information stored in a digital format. EHR is a great tool healthcare have, it can be mighty effective if used in a right way.
US government is pushing the health care providers to use the EHR effectively, initially they mandated the use of EHR, then they announced incentives for the health care providers who effectively use EHR.
The US EHR adoption has almost doubled from 42% to 87% from 2008 to 2015.
The biggest challenge organizations have is pulling out data in a way that it provides meaningful insights.
Storing data in cloud and integrating it with visualizing tools is one solution.
2. Asset Tracking
Hospital has valuable machines for which regular maintenance is required, if the machines are not taken care regularly it may lead to major repairs, given that all these machines are expensive.
In order to avoid these expense, the past performance of the machine and the repairs can be analysed to predict the future maintenance (Predictive analytics), the cost involved etc..
3. Data Management
As explained at the beginning, data in health care is collected from various places and there is no single source of truth, it is difficult to understand patient health status when the data is stored at different places and it is not properly visualized.
Say the patient is moved from one ward to the other, data transfer in most cases are not done in Realtime, so it takes time to reflect, if there is any mismatch in data, it becomes difficult to pull out the data.
The solution for this challenge is to migrate to cloud, so that all the data is stored in one place and it is in real time. This will benefit both the doctors and the patients as it gives a 360-degree view of the patient.
Data collection does not provide any value unless it provides valuable insights. Data visualization helps you understand what happened and why it happened, and it assists in making data driven business decisions. Initially reports and charts were created from the historical data, past data is just the first step. For advanced visualization and to understand the story from the real time data, various visualization tools can be used (Tableau and Power BI are being used across industries).
5. Rising costs
Despite being a sector with immense growth, rising cost is always been a issue that is being faced. Healthcare cost in United States are high compared to other countries, this is due to expensive diagnostic tests, administrative cost and high cost drugs.
The rising healthcare cost increase the health care premiums which makes it difficult for the employees to pay the premium.
Reducing health care waste and reducing readmission can be few ways to reduce the rising costs, this can done with the help of big data analytics and health care analytics.
6.Clinical decision support systems
Clinical decision systems assist physicians to make best clinical decisions, few hospitals still don’t use advanced CDSS, the decisions they make are just with the past record of the patients.
This CDSS can be of two classes one can be knowledge based and the other being non knowledge based.
Knowledge base fetches information from the repository of data it has, which is collected from various sources. Whereas non knowledge-based uses machine learning algorithms with minimum data sets to make decisions.
7. Staffing Challenges
Finding trained staffs is one among the toughest challenges that health care face, identifying the required number of specialists is one amongst the problems. Many of the organizations fail to predict the number of patients who would likely to visit the clinic.
Predictive analytics can be used to find out the expected number of patients who would turn up and then plan the staffing in prior. It is also possible to find the demand for physicians.
Data driven insights will help in improving the patient experience.
8.Supply Chain Management
Supply chain management in healthcare largely differs from other business as the stock cannot be stored for long, according to a recent research supply chain is one of the important issues that the health care heads are looking to reduce the cost. According to a survey, hospital could save up to $23 billion by implementing data driven best practices and on an individual basis hospital could reduce total supply chain costs by 17.8% by effective use of data analytics.
Data is the key to make successful business decisions across industries, write to us at email@example.com to know more of how we have helped clients in leveraging data to make successful business decisions. Stay tuned for our upcoming blogs to know real time use cases and applications of advanced analytics in healthcare industry.