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A healthcare system can operate smoothly only if it can approximately predict the number of patients to be treated at any given period of time. A proper count helps to keep all the critical management aspects under control. Yet, hospitals are struggling to bring this key dimension under control. The fall out of fluctuation in patient demand affects both the quality of treatment and expenses. The havoc caused by the Covid 19 pandemic is a good example in sight.  The number of patients increased by leaps and bounds, compared to the number of clinicians available for treating them. Aside from this, the infrastructure, clinical protocols, and informational technology of every hospital came under severe stress leading to an uncontrollable situation. The one thing that this pandemic has taught is that no level of protocols and infrastructure can guarantee smooth operations unless a hospitals can predict patient count who come for treatment.

How Analytics Sets the Equation Right for Hospitals

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In a technology-driven world, hospitals are getting control over this confusion with the help of healthcare analytics. It is the process of analysing hospital data to forecast trends, expand outreach, and better manage daily operations. As a technology, analytics offers  insights both on the macro and micro level thus helping uncover paths to improvement in care quality, diagnosis, and costs management. So how does it actually achieve this? 

By providing information to reduce the gap between perception and reality, eliminating decision-making based on gut feeling, and empowering ground-level managers with adequate data-based insights.

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Having said that, it needs to be emphasized that hospitals can leverage analytics around four pillars of medical operations. These include clinical, financial, operational, and administration.

Clinical Benefits of Analytics

Let’s take the case of a patient whose blood pressure keeps vacillating because of different reasons, and the doctor needs to keep a real-time tab of when it spikes. When there is an alarming increase, an analytics driven system will send an instant alert to the doctor and the doctor can reach out to the attendant in time and advice on the doses to be administered to arrest the spike. Likewise, Asthmapolis, uses inhalers with GPS-enabled trackers to scout for asthma trends both in individuals and with the population at large. The data generated is made available on a general database of public health and therefore can be analysed to find improved treatment solutions for asthmatics.

Aside from improving clinical efficiency, there’s yet another big benefit and often overlooked of real-time analytics i.e. improving patient doctor synergy. While on the one hand analytics encourages patients to be more involved in their own treatment process, on the other hand physicians can use the all-round view of the patient to go broader and deeper in medical services. It then becomes possible to deliver a more comprehensive treatment with better outcomes and greater customer satisfaction.

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Here’s another example. Using predictive algorithm, a psychiatric team found that attempts to commit suicide was 200 times higher among the most severely afflicted psychiatric patients. These patients include those who suffered from substance abuse, acute mental health, failed suicide attempts, use of heavy psychiatric medications etc.  Several psychiatric hospitals have demonstrated that they can use data analytics to accurately identify people at high risk for suicide attempt or suicide death.

Likewise, in the field of radiology, analytics can bring about significant transformation in the way image readings are done. There are applications that help to replace images with numbers and then carry out algorithms to dig into the data for a better outcome. So, unlike the traditional process of image evaluation, analytics focuses on each byte and bits contained in the data to make interpretations more precisely accurate.

Operational Benefits of Analytics

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Once analytics-driven clinical efficiency is achieved, it is possible to provide proactive treatment to patients from remote. A direct fall out of this is reduction in hospital readmissions. Imagine having a similar analytics-driven process to manage the Outpatient Department (OPD)! It can uplift operational efficiency of the department by leaps and bounds.

No matter whether it’s an hospital a clinic, the OPD is the busiest department of a healthcare provider with patients having to wait in long queues. And to top it, it’s a slow-moving process consisting of sequential steps such as patient registration, primary screening, checking HMO and other payer coverage, lining up in the doctor’s waitlist for the one-to-one interaction and finally queuing up for buying drugs. 

With analytics a lot of these can be streamlined in effective ways. For instance, analytics helps to predict the number of outpatient visits with 90 percent accuracy. In fact, analytics tools can be used to precisely tell the time of overcrowding. Based on this knowledge, hospitals can better measure and predict administrative and clinical staffing levels for the outpatient department. At the inpatient department this knowledge helps to understand the daily estimate of surgeries and therefore the number of operating rooms that needs to be made available.

Another important use is the computation of bed availability in advance. This is a very difficult task, as it involves a number of variables such as number of beds, gender, patient age and condition, treatment and nursing schedule, type of bed needed etc. Analytics solutions can be used to predict discharges and provide actionable suggestions several days ahead. Hospitals are known to leverage analytical solutions to reduce bed scheduling by 30 to 50 percent. This translates into better utilization of available resources with no need to cancel surgeries for lack of beds. For a busy practice this means non-stop cash flow.

Administration Benefits of Analytics

In today’s scenario the top administrative challenges faced by hospitals is recruiting top nursing talents, expanding the use of Telehealth, keeping rising costs under check, and managing insurance. Analytics plays an important role in ironing out all these challenges.

For instance, with recruitment analytics can track, measure, collate and analyze candidate data to make better hiring decisions. As a result, hospitals are always in a position to improve quality of hire, optimize recruitment costs and improve diversity and make accurate forecasts.

So how does this work exactly. Analytics tools bank on rich dataset that comprises hundreds of thousands of hired cases collected over a period of time and reflects a vast section of the heterogeneous populations. Analysing the diverse data set helps in overcoming all the drawbacks common to hiring such as biased hiring policies and improper placement decisions. Today, with the availability of adequate and appropriate data, a hospital can analyse the entire hiring process in the right perspective and also use it to plan for future recruitments.

Integration of data analysis in processing third-party payer claims can help providers align their revenue cycle process with their long-term goals. Data analytics helps them accurately analyze the claim patterns, how internal trends are evolving, and identify the areas that need improvement. Besides, it also helps in identifying external trends that may have an impact on their claim outcomes, reduce the overall time taken for insurance processing, and reduce the costs involved.

Much of the benefits of an analytics driven administration process helps in reducing overall costs of operation for a hospital. For instance, the federal government now imposes a fine on hospitals for excessive readmissions. This may be up to 3% of the Medicare expenses and can add up to be a big sum of money. With the help of analytics, healthcare providers can take correct steps to stop a patient from returning within the 30-day window. Likewise, accurate prediction of hospital staffing needs help providers labor costs as well as make shift management more cost effective.

Financial Benefits of Analytics

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Before we delve into this, lets digest the facts. Each year, healthcare providers in the US get around $8 to $10 billion in claims out of which close to 10% of claims are either delayed or denied. At an average, a hospital, stands the risk of losing out $5 million in payments every year. Out of this 63 % can be recovered, but a cost. Each of these denied claims costs the hospital around $118 for a reclaim.

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Now all of these undue expenses can be put behind with predictive analytics. Predictive analytics gives a heads up to providers on the possibility of a claim being denied before it goes to the payer. Billing agents get a chance to correct the claims before the submission, thereby reducing the chance of clean claims denials.

How exactly does analytics help eliminate this? By identifying missing information that can lead to rejections, by validating codes to back a diagnosis, by verifying coverage and seeking authorizations before procedures, by checking the accuracy of fee schedules, and following up with payers on re-submissions. In other words, with analytics, it is possible to have insights that can be implemented to increase cash inflow, reduce A/R days, and make collections more effective.

In some hospitals, analytics has been implemented to streamline the process of denial management. For instance, by segregating denial types, a hospital is able to dedicate staff on high-value denials. This way they have been able to clear 90% of claims within 90 days. Other significant benefits realized by providers include resolving claims in single follow-up, reducing accounts receivables from anything between 23 to 30 percent, and increasing point-of-collection margins by more than 10%.


Before you embark on adopting analytics for your healthcare business, you need to have the right foundation. You must have a clear idea of what is your problem and what is it that you need to know to solve it. A mix of descriptive, predictive, and prescriptive is what you may be needing. But we want you to go beyond and adopt adaptive analytics so that you can use data real time to understand changing clinical, administrative, financial, and operative requirements.

Who We Are and What Makes Us an Expert?

MedBillingExperts has over 10 years of experience in providing comprehensive healthcare analytics outsourcing services to healthcare providers in the USA. Our solutions have assisted our clients improve clinical outcomes, risk management, and administrative efficiency.  Driven by a team of data scientists, BI managers, informatics specialists, we provide specific solutions to be deployed across strategic business functions of a healthcare provider.


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