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Healthcare analytics is the process of analysing historical and real-time data to accurately understand trends, enhance outreach, and check the spread of disease. The technique involves extracting multiple transactional data, mapping the data, finding discrepancies, and taking steps to resolve them in a controlled way. Large healthcare providers in particular leverage analytics both at a macro and micro level to improve patient care quality and business management.  But, before we delve into the importance of analytics in healthcare, let’s have a look at how the technology finds application in healthcare centers.

healthcare analytics services

Health Analytics – A Few Use Cases

Palliative care is the process of mitigating the injury risks to sequela patients.  It primarily includes care giving tasks to patients with severe illnesses under the observance of physicians. Palliative Connect is an advanced technology that uses machine learning algorithm to extract data from EHRs and analyze few very important parameters to predict the condition of patients and the probability of a deterioration. This helped doctors get prepared in advance to meet a patient’s needs.

Data Analytics helps in triggering an appropriate and early response from emergency teams at the point of care. A hospital leveraged it to reduce adverse events by 35%, and cardiac arrest by more than 86%.

Here’s yet another instance. A children’s hospital had to deal with underutilization of available space for several years. This led to the fall of the hospital’s revenue and impacted the proper care of patients. The hospital made use of an analytics driven solution to solve this problem. The solution involved creating an interface to provide a visual presentation of the available space. By tapping EHR data, they could calculate occupancy rate in advance. The solution relied on appointment time, available space and employees to find out the required space. The visual representation gets updated every time there is a new request. By incorporating analytics into its daily operations, the hospital was able to accommodate 550 additional appointments and jack up its earnings by over $80 thousand within six months.

Sensors in an MRI scanner can emit early warning signs of possible technical issues leading to timely repair or replacement. Data from equipment will also be leveraged to update real-time data to forecast future utilization.

Inference from the Use Cases

The one thing common in both this cases is proper utilization of available data to predict what might happen. Healthcare analytics involves collecting all available data and extracting insights from it to predict health conditions of individuals. Given that more than 95 percent of health facilities in the US rely on Electronic Health Records (EHR) systems, it means a large amount of data is already being stored digitally on a daily basis. Further the bulk of the of data is generated from widely adopted IoT devices used in most of the hospitals in the US today.

The ability to merge data can lead to miraculous results. Once merged and displayed, the data can guide the staff based on facts and not guess work. Like front office staff even back office staff can tap into this data to find out correct answers to understand questions like “What is likely to be the total revenue throughout the enterprise?” Not only that they can even drill down to see all the component parts that make up that revenue number.

Healthcare analytics can combine data from electronic medical records, medical alert services and fall detection pendants to identify extremely sick patients who are most likely to require emergency.

How Healthcare Analytics Helps Healthcare Providers

healthcare analytics service providers

Today, healthcare needs a research-driven approach based on unknowns. This approach is the only way by which it is possible to create adaptable, flexible, and scalable solution to acquire intelligence from fast generating data. Data analytics can be of great help in the following ways:

  • Index information from any source type including social media, feeds, databases and file shares
  • Establish relationships between fast evolving data entities
  • Combine structured and unstructured data
  • Customize environment based on individual needs and data sets
  • Enable search algorithms for significance, relevance, temporal decay and geo-spatial decay of data

However, a comprehensive data capture should also be accurate enough to generate accurate reports or raise correct alerts. Advanced technologies such as AI and Machine Learning can be widely used to automate the analysis of medical data around the globe. According to the American Hospital Association (AHA), the biggest advantage of AI is that it helps in managing unsustainable workloads and is capable of giving highly accurate results.

Yet another report by McKinsey Global Institute suggests the use of data analytics will help US healthcare providers to create more than $300 billion in value every year out of which two-third would be realized through savings.

Specific objectives that can be addressed by analytics.

Make Clinical Care More Effective

  • Improve quality of care
  • Reduce medical errors and improve patient safety
  • Improve disease management, wellness and prevention
  • Understand clinical performance and physician efficiency
  • Acquire new talent and improve customer satisfaction, acquisition and retention

Make Operations More Effective

  • Cut down on costs and improve efficiency
  • Optimize patient enrolment and network management
  • Improve performance-based pay programs and accountability
  • Increase operating speed and adaptability to sudden developments

Make Administrative and Financial Performance Sound

  • Improve revenue inflow and ROI
  • Optimize resource and equipment utilization
  • Optimize supply chain and inventory management
  • Improve regulatory compliance and risk management
  • Detect fraud and abuse

Common Types of Data Analytics in Healthcare

The common type of analytics that can be used in healthcare include:

Descriptive analytics – To determine how fast a virus can spread by evaluating positive test rate in an area over a period of time. In case of patient treatments it provides real-time data with all the corresponding statistics which includes volume, date of occurrence  patient details, etc. Similarly it can be used to identify the reason behind a rise in the number of denied claims over the few months. This type of analytics is commonly presented in the form of  charts, graphs, reports, and dashboards.

Diagnostic analytics – To diagnose a patient with a particular illness based on their current symptoms. In other words, diagnostic analytics takes descriptive data a step further and analyzes questions from a deeper perspective as to why did this happen? For instance, it takes into account all the symptoms— dry cough, fever, and fatigue—to point at the possible infection. In the same way diagnostic analysis can be used to identify an increase in denials specific to a particular provider. To sum it up it can be leveraged to carry out root cause analysis.

Predictive analytics  – To predict the spread of a seasonal disease by analysing case data from previous years. This kind of analytics takes historical data into account and feeds the information into a machine learning model to identify key patterns and trends. Once the model is established it is mapped with the current data to forecast what is likely to happen next. In a pandemic situation like Corona, predictive analytics can be used to predict a surge in patients admitted in the next several weeks.

Prescriptive analytics- To determine the risk in developing future conditions by assessing a patient’s pre-existing conditions, understanding the risk of developing future conditions, and providing preventative treatment plans with that risk in mind. In other words, it is the next step to predictive analytics that gives you an idea of what is likely to happen in the future, and what should you do to tackle it. It comes up with various courses of action as well as underlines the potential implications of each type of actions.

Forward-thinking healthcare providers bank on a variety of analytics to make game changing decisions. However before you embark on an analytics drive you need to have a clear idea of the defining issues and desired outcomes. When these two critical aspects are properly aligned, you can bring the right targets and objectives into focus – both from growth and performance wise.

How Providers Are Using Healthcare Analytics:

  • Top Providers
  • Mid Size Providers
  • Develop Future Strategy
  • Top Providers – 60%
  • Mid Size Providers – 30%
  • Product Research and Development
  • Top Providers – 70%
  • Mid Size Providers – 20%
  • Marketing and Sales
  • Top Providers – 60%
  • Mid Size Providers – 25%

Who Are We And What Makes Us an Authority in Healthcare Analytics?

MedbillingExperts has over 10 years of experience in healthcare solutions. Our analytics solutions are designed to help organizations use analytics to unlock and apply new insights from fast emanating data. Our scope of analytic service includes determining how to apply analytics to a providers current set of challenges so that they can gain correct insight and achieve faster time to value. Contact us now to know more about our solutions.


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