Defining Data Analytics for Hospitals
This article was written by Lisa Miller.
Table of Contents
The following topics are thoroughly covered in this article:
- What is healthcare analytics?
- Examples of analytics in healthcare
- How analytics benefits providers, patients and payers
- How data analytics can support your hospital or healthcare systems’ cost savings and financial improvement goals
What is Healthcare Analytics?
Analytics is becoming increasingly prevalent across nearly every industry, which is a trend that’s notably apparent within the healthcare industry. The provision of modern treatment methods and techniques relies on data analytics. When implemented correctly, analytics can predict outbreaks, bolster preventive care, enhance the overall quality of care that patients receive, and lessen the costs associated with treatments.
When you hear the word “analytics”, this means that an ample amount of data is being digitized and further consolidated, which helps with analysis of the data. In disparate pieces, the data may not mean much. However, when put together, the information can tell a story that allows healthcare professionals to treat illnesses early or identify the initial warning signs for a disease.
By improving the lives of patients, data analytics also helps to reduce expenses for hospitals. Healthcare analytics involves numerous intricacies that differ from other industries. For instance, real-time vital signs and electronic health records are taken into account when performing analytics. This data can be added to social determinants of health data to proactively manage patients when they leave a hospital. Because of the complex processes involved with healthcare analytics, an agile analytics solution is necessary that is supported by subject matter healthcare experts.
By improving the lives of patients, data analytics also helps to reduce expenses for hospitals. Click To Tweet
Examples of Analytics in Healthcare
To better understand healthcare analytics and all that it entails, it’s important to take a look at various examples of analytics in healthcare. The comprehensive data that can be collected through healthcare analytics can be converted into numerous insights. The following are some top examples of how analytics can be used within the healthcare industry.
The key to preventive care is to identify risk factors in someone who may be susceptible to a specific condition or disease. For instance, anyone who smokes cigarettes on a regular basis will have a higher risk of developing lung diseases like COPD, asthma, and lung cancer. Healthcare analytics can be used to effectively identify risk factors that might not have previously been tied to the disease or condition in question.
Back in 2017, Blue Cross Blue Shield analyzed many years of pharmacy and insurance data pertaining to opioid abuse and overdose. Through this analysis, they were able to identify nearly 750 risk factors that accurately predict if someone is at risk of abusing opioids. Gathering all of this data was only possible with the help of analytics experts and the right software solutions.
In the same vein as preventive care, data analytics is also being used for predictive purposes. Companies like Optum Labs have collected electronic health records of millions of patients, which have then been stored in a database. From this database, it’s possible to use the data to strengthen patient care and make it more efficient. When analytics are used in a healthcare setting, doctors can make immediate decisions that are driven largely by data that’s available through analytics. In certain situations, this data can be used to predict the initial onset or eventual progression of various conditions.
One manner in which hospital data analytics can be highly beneficial is with staffing. Without the use of analytics, staffing issues can result from under-staffing or over-staffing. When the hospital is under-staffed, the patients aren’t provided with the level of care that they require. On the other hand, over-staffing issues result in higher expenses and a loss of money for the hospital. Using data analytics allows your hospital to successfully avoid such problems by analyzing months or years of hospital admissions records.
When healthcare data analytics is performed on these records, it’s possible to identify patterns and accurately predict what admissions will be like at the hospital around 7-14 days in advance. This data can then be used to schedule a higher amount of staff during times when greater admissions are expected. This should lead to less wait times for the patient as well as better overall care.
Patient Engagement Improvements
Using data analytics, healthcare has been shown to improve patient engagement as well. A large number of prospective patients wear smart devices that are able to track numerous metrics like sleep patterns, heart rates, and number of steps taken. Through these metrics, patients are monitoring their overall health. It’s possible for the data that’s collected from these devices to be uploaded to the cloud, which provides doctors and physicians with the ability to look over the data and draw conclusions from it.
If a patient suffers from hypertension or asthma, the data that’s sent to the physician from the wearable device can be used to identify the proper treatment plan and to make sure that the patient is taking the steps necessary to correct their symptoms and lessen their need for medical care. When used correctly, patients should be able to reduce the number of visits that they need to take to their primary doctor. The doctor could also identify a potential risk factor or problem with the data that requires further treatment on an in-person basis, which could allow for preventive care to be administered.
Electronic Health Records
Hospital data analytics is mainly used for electronic health records, which are currently kept by nearly 95 percent of all hospitals across the U.S. A digital health record is an extensive record that includes even the smallest of details pertaining to the patient’s health. Along with standard demographic information and medical histories of family members, electronic health records also document allergies, medications, test results, and past diagnoses.
Each record is comprised of a single file, which makes it easy to avoid duplicate records and excess paperwork. These records can be kept for many years without needing to worry about a loss of data or similar problems that could result in incomplete records. Electronic health records are only possible with data analytics healthcare, which provides hospitals with a simple tool for the collection and analysis of copious amounts of data.
Keep in mind that a core benefit of electronic health records is that they can be instantly accessed by different healthcare providers and hospitals, which eliminates the need for sending patient information from one medical facility to another. With the kind of data that’s collected in an EHR, lab tests, and office visits, a patient’s healthcare can be optimized.
How HealthCare Data Analytics is Used by Medical Providers
In the past, most healthcare providers relied on a fee-for-service model that was straightforward and efficient in practice, but had its drawbacks. With this particular model, doctors received very little incentive to provide value-based care that was more holistic in nature.
Without using hospital data analytics, the value-based care model can be more inefficient than its fee-based counterpart. Hospitals are required to improve efficiency and day-to-day operations with the value-based model, which is where analytics is used. When an analytics solution is implemented, medical providers should be able to:
- Improve performance with quality care that’s backed up by data
- Enhance quality of care and patient satisfaction by automating processes like processing insurance and making appointments
- Perform predictive analytics for at-risk patients, which lessens readmission rates
When looking specifically at patient care, data analytics healthcare can use patient histories and patterns to determine which treatments are necessary. Predictive analytics can hone in on patients who currently suffer from chronic conditions and may be more at risk of being involved in an emergency medical situation. The decisions that are made from this data can increase corrective care while decreasing the number of emergency visits.
Keep in mind that human error is another significant problem that hospitals face when administering medical care. The majority of insurance claims that are successfully filed against a hospital are brought about by human error, which could involve anything from an incorrect diagnosis to the wrong dosage with a medication.
When these errors occur, hospitals and other healthcare facilities will be tasked with absorbing some of the claims and insurance costs that arise. Hospital data analytics can look over patient data and any prescribed medication to alert doctors and patients of incorrect dosages or wrong prescriptions, which lessens human error and the cost to your hospital.
How Healthcare Data Analytics Helps with Population Health Management
With population health management, a greater focus is being placed on predictive and preventive care as opposed to reactive care that centers around a response and treatment. When predictive analytics are implemented, a chronic illness could be identified while still in the early stages. Catching illnesses early on reduces the costs of care and lessens the possibility of repeat hospitalization. Some of the data that’s collected and analyzed with data analytics healthcare extends to claims data, health data, social factors, and the results from lab testing.
When large amounts of data are collected and analyzed, healthcare data analytics can:
- Measure potential patient outcomes to determine which treatments and programs have the highest likelihood of success
- Track patient satisfaction and provider performance to better help with resource allocation
- Provide the correct patient/provider ratios depending on the situation and number of admissions
The use of analytics in healthcare assists with keeping financial mismanagement at bay. By reducing over-staffing and under-staffing issues, patients should be provided with the appropriate level of care without long wait times. More efficient care equates to higher patient satisfaction and lower bed shortages.
How data analytics can support your hospital or healthcare systems cost savings and financial improvement goals
These are four ways a hospital can utilize data analytics to achieve cost savings:
1. Run 12 to 18 month PO detail report and benchmark each line item
- This should be performed on a quarterly basis because pricing does change frequently on supply and implant expenses and because new products are utilized; this is a great way to identify off contract spend before it increases your costs unnecessarily.
- The key to getting best pricing is to have a constant quarterly monitoring review and even from 2 different benchmarking resources; different companies will have access to different data and you want to see all of the opportunities available to your organization.
2. Identify utilization improvement opportunities through comparing physician variations in testing, supplies and specifically implant costs in the O.R.
- Physician engagement is a leading (and underutilized) strategy for hospitals to deploy.
- Given the right data and understanding of the benchmarks, they will be one of the best resources your hospital can utilize for cost savings.
3. Analyze your professional hourly rates to identify ways to reduce these outsourced hourly fees and standardize throughout your hospital and health system.
- Organize hourly rates for professional services by specific category and have a one hourly rate allowable for specific services. Project management is one hourly rate, technical analyst is another rate, etc.
- Engage an outside expert resource to provide you with accurate and competitive hourly rates. Most organizations have the ability to save over 20% in this area of spend with a focused initiative on hourly professional services rates and having the right benchmarking partner to achieve better pricing.
3. Take an area of spend in your purchased services, like regulated medical waste or medical records or specialty reference lab, and analyze the last 12 months of invoice line item spend for utilization improvement, invoice accuracy to the agreement, and pricing opportunities that can be achieved through an experienced benchmarking resource.
- Since services spend is detailed only on an invoice, the best approach is to work with a company, like VIE Healthcare, who has patented technology to extract and analyze your purchased services invoice line item details.
- Review ways to reduce utilization or consolidate if you have multiple service vendors providing the same service.
At VIE Healthcare Consulting, we focus on delivering non-labor cost savings. We are the preferred advisory solution for hospitals and healthcare systems to increase operating margins, reduce costs, and improve efficiencies.
We are a team of margin improvement experts, cost consultants, healthcare business analysts, data scientists, technology specialists, and clinicians. We deliver real cost savings and financial results – since 1999 we have saved our clients over $1 Billion in non-salaried expenses.
If you are interested in learning more, the quickest way to get your questions answered is to contact one of our margin improvement experts or email Lisa Miller at email@example.com.