It's no secret that the Healthcare Industry is an expensive one. According to the Center for Medicare and Medicaid Services of the United States, U.S. health spending increased to 4.3 percent to reach $3.3 trillion, or $10,348 per person in 2016 alone. Because of this, Healthcare companies are looking to make strategic decisions to offer solutions to improve efficiency and cut down costs in the Healthcare sector. Many higher ups in the industry are turning to Big Data solutions to solve these problems. Here are 5 ways Big Data can help improve medical operation efficiency and lower costs.
For hospital shift managers, one of the the most common problems is have the most optimal amount of staff at the hospital for a given period of time. Too many workers can lead to increased operational costs, and too few of works can lead to poor patient care. Many staffers have turned to Big Data to solve this problem. Many hospitals have started using analytical data to predict the number of patients at the hospital in a given time. Big Data allows them to go as broad as how many patients they see a year to how many patients they see on an hourly basis. Data scientists can use over 10 years of hospital admission records to predict patterns in admission rates and predict future trends. Many hospitals now have a web based interface that can allow them to forecast admission rates over a 15 day period. This can drastically cut down on hospital operation costs.
Did you know that just 5% of patients account for half of all U.S. healthcare spending? This is why Healthcare professionals are turning to Big Data to create a system to predict which patients are most likely to be high costs. This allows them to track and identify the sickest and most expensive patients in a positive way. When they know which patients have the highest-risk factor, the can proactively monitor target these patients early to prevent more drastic and costly hospitalizations by monitoring them more frequently and making sure then don't miss any appointments or treatments down the road.
According the Healthcare IT News, as many as one-third of readmissions may be preventable. This offers a big opportunity to cut down on costs and improve care efficiency . Healthcare providers can use algorithms to look at admission data to predict who is likely to be readmitted. This allows physicians to focus on individual treatment for the patients who need it most, and allow them to monitor after discharge to ensure they don't develop issues that cause they conditions to deteriorate.
To keep costs down, it is important to manage systemic problems for chronic diseases affecting multiple organs. Studies show that autoimmune disorders like rheumatoid arthritis and lupus would benefit greatly from big data. Big Data can allow caregivers to deliver high cost therapies in a more targeted way. Big Data can help predict the course of a patient's disease and tailor those treatments allow the way. With the rise of electronic health data, this has been a growing area for improving patient care and lowering costs.
Being able to capture and gain insights from physician data has had a major effect in optimization medical efficiency by improving patient outcomes and reducing overall healthcare costs. Many hospitals are now beginning to track the performance of individual hospital doctors and outpatient providers. This reflects on a physician's ability to reduce readmission, mortality, and complications involved with outpatient care. Looking at this data can point to areas for certain hospitals that need improvement, and allows them to allocate more resources to that area.
Sources:
https://www.healthworkscollective.com/how-big-data-analytics-in-healthcare-saves-lives/
https://www.healthcareitnews.com/news/6-ways-big-data-can-lower-costs
https://datafloq.com/read/5-ways-big-data-reducing-healthcare-costs/89
https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/downloads/highlights.pdf
1. Intelligent Staffing
For hospital shift managers, one of the the most common problems is have the most optimal amount of staff at the hospital for a given period of time. Too many workers can lead to increased operational costs, and too few of works can lead to poor patient care. Many staffers have turned to Big Data to solve this problem. Many hospitals have started using analytical data to predict the number of patients at the hospital in a given time. Big Data allows them to go as broad as how many patients they see a year to how many patients they see on an hourly basis. Data scientists can use over 10 years of hospital admission records to predict patterns in admission rates and predict future trends. Many hospitals now have a web based interface that can allow them to forecast admission rates over a 15 day period. This can drastically cut down on hospital operation costs.
2. Predicting High-cost Patients
Did you know that just 5% of patients account for half of all U.S. healthcare spending? This is why Healthcare professionals are turning to Big Data to create a system to predict which patients are most likely to be high costs. This allows them to track and identify the sickest and most expensive patients in a positive way. When they know which patients have the highest-risk factor, the can proactively monitor target these patients early to prevent more drastic and costly hospitalizations by monitoring them more frequently and making sure then don't miss any appointments or treatments down the road.
3. Preventing Readmission
4. Treatment Optimization
5. Monitoring Physician Performance
Being able to capture and gain insights from physician data has had a major effect in optimization medical efficiency by improving patient outcomes and reducing overall healthcare costs. Many hospitals are now beginning to track the performance of individual hospital doctors and outpatient providers. This reflects on a physician's ability to reduce readmission, mortality, and complications involved with outpatient care. Looking at this data can point to areas for certain hospitals that need improvement, and allows them to allocate more resources to that area.
Sources:
https://www.healthworkscollective.com/how-big-data-analytics-in-healthcare-saves-lives/
https://www.healthcareitnews.com/news/6-ways-big-data-can-lower-costs
https://datafloq.com/read/5-ways-big-data-reducing-healthcare-costs/89
https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/downloads/highlights.pdf
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