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It is understandable that anyone can feel intimidated by the huge influx of data that keeps flowing through healthcare systems every second of the day. But to draw meaningful insights from it, and use it to treat patients and prevent diseases is a big step in healthcare. This is what Big Analytics or Advanced Analytics does. The data you receive on your machines is just a jumble of ones and zeros, and you have no use for it unless you can understand what it is trying to convey.
According to the survey conducted by Health Catalyst, a whopping 90% of the respondents admitted that analytics is going to be either “extremely important” or “very important” to their organization within the next few years. And the respondents also rated the importance of healthcare trends and the role played by analytics in them.
Analytics is thus becoming very crucial in tracking different types of healthcare trends. Advanced analytics touches every aspect of healthcare software systems including clinical, operational and financial sectors.
Healthcare data management is the process of analyzing all the data collected from several sources. This helps the healthcare organizations treat their patients in a holistic manner, provide personalized treatments and enhance health outcomes. The healthcare marketplace has become increasing competitive and complex, and it gets more complex by the day.
There must be efficient tools and methods to create value from the data. By collecting the data that’s arriving, the technologies in the market can help you make informed decisions leading to improved quality in healthcare. Organizations are getting sufficient data that helps them understand what the patient needs are.
As they go over the analytics they get the bigger picture of the patient’s condition, and eventually, they are able to give precision driven care and treatment. This eventually leads to end-to-end process optimization and increased competitiveness. The diagram given below explains this in an elaborate manner.
Healthcare organizations all around the USA have begun to adopt technologies like PACS imaging systems ,Telemedicine Services, EMRs or Electronic Health Records solutions that attempts to make sense of the massive data that flows through the system (both structured and unstructured). Hence, it is important to know what are the tools that can extract information from the data to generate value and enjoy operational, financial and clinical insights.
There are genome analyzers and other analytics tools in the market that help in understanding the facts, and eliminate unwanted/useless details to extract only what’s needed. The end result of this is better clinical outcomes for the patient. There are several ways in which the healthcare organizations can make use of information collected through various sources.
Healthcare analysts work diligently on the data that’s provided to them. Hence, they scour both structured and unstructured data, including the data that’s readily available on non-traditional channels like social media messages, text messages and the like to discern any patterns. They convert all the information into actionable insights and work towards achieving better health outcomes.
The proliferation of mobile devices has really helped in this regard too. It helps the analysts understand the path of infectious diseases (through the GPS coordinates obtained from the cell phone). This was how they were able to take preventive measure during the Ebola outbreak in West Africa. Cell phone mobility data can help them understand not just present cases of outbreak and infectious diseases, but it will also shed light on diseases could spread in the future as well.
The process of using and converting the data:
- Study the patterns presented in the data and check for any disease outbreaks. By correct analysis, the caregivers will be able to provide treatments and even respond to medical emergencies.
- Use the analyzed data to come up with preventive techniques, medicines and vaccines.
- Work towards preventing the spread of the crisis and reduce mortality rates caused by the disease by checking where prompt care should be provided.
It was difficult to control epidemics in the past because of lack of timely data, disparate datasets that you cannot collate, lack of experts with computational background who can help in epidemic planning, control and response. With big data analytics, the challenges and epidemics cans be monitored.
For example, genome sequencing gives out huge quantities of big data, and you can use powerful analytics that would help you watch how microbes mutate during an outbreak in real time. Nexstrain is a tool that enables the sharing and tracking of genome sequences as and when they happen to prevent and control outbreaks.
Organizations depend on the expertise of the healthcare analytics to collaborate the data collected from various sources to monitor the efficacy of their processes. This would help them understand how the patients have responded to their program and what their condition is presently.
Here are some areas where healthcare providers can enjoy the advantage of predictive analytics and healthcare informatics:
- Recognizing those patients that are likely to develop diseases or possess certain risks to their health.
- Develop specific wellness programs that can be catered to serve the interests of patients, so they can enjoy improved health.
- Identify inefficient programs and processes that do not generate desired results, so they can be removed completely. This ensures that only result-oriented programs are kept in the wellness program packages. The rest are all removed from it.
- In certain cases, the patient may have to be readmitted due to a relapse or adverse effect. The analytics will be able to identify what caused the relapse, and can make suggestions on how to prevent it.
- It is possible to optimize usage of resource, increase productivity, and throughput after analyzing.
While predicting the outcomes for the patients, the analytics will also consider the latest medical research through all the peer-reviewed journals and databases. The predictions can come up with analysis that the human brain can never perceive or suspect. This is why the predictions range from how the patient responds to medications to hospital readmissions.
Artificial intelligence is also used to create a production profile (algorithms) collected from previous patients. A prediction model is created using this technique. This technique is then deployed to help new patients to get a new diagnosis.
Key takeaway 1: Analytics can drive improvement in patient outcomes while providing in-depth information on clinical performance
It is not easy to get a drug out to the patient. There is a thorough and overwhelming process of creating the drug, taking it through elaborate clinical trials and then finally, approval from the FDA. Every pharmaceutical company and healthcare provider must strictly go through this process before administering medicines to the patients. Companies use predictive modelling, statistical tools and algorithms, and healthcare analytics to shorten the time a drug stays in the R&D pipeline. The advantages are stated below:
- Advanced analytics play a major role in developing a low-attrition, leaner, faster and highly productive R&D pipeline.
- Looking for methods that would accelerate the process of drug development to improve patient health. Try procedures that would prevent failures in the clinical trials and improve patient recruitment processes.
- Analyze the patient records to see if there were any drug interactions in the past when new drugs were introduced in the market, and to identify the effects of such drugs.
In order to have a very fast productive pipeline, the healthcare organizations must have sophisticated tools and techniques that will help in analyzing all the information that’s coming in.
The healthcare industry is going through serious metamorphic changes as it moves from a volume-based business to a value-added business. Healthcare providers are under tremendous pressure to provide value-based care with better outcomes to the patients. This has in fact led to change in the cost structure, increased life expectancy and better control over chronic illnesses and infectious diseases.
The benefits are not only for the patients, because it has touched several other entities as well - facility providers, insurance providers, government entities, etc. For example, insurance companies are moving their models from a fee-for-service to value-based data-driven payments by making use of EMRs. Electronic Medical Records can ensure high quality patient care.
Together all these entities touch upon these areas:
Disease Intervention and Prevention - Prediction of disease and their prevention techniques often go hand in hand with analytics. This way, the organizations will be able to identify patients with high risk of developing serious diseases quite early in the condition and provide them with better outcomes so they don’t have to face long-term health problems. This prevents long term care which could mean costly treatments and complications that might arise.
Care Coordination - Analytics helps in delivering care coordination and this is really helpful in emergency care or intensive care. Especially, when there should be quick response time. This can save the patient’s life. Apart from deploying care coordination strategies, analytics can even alert the caregiver of a readmission to the hospital in a 30-day window.
Customer Service - Excellent customer service is extremely important in healthcare, and any discrepancies in that could prove to be fatal. Analytics can deeply impact customer service. And it helps you provide a personalized touch, understand the patient’s needs accurately and even motivates them to improve their health. The personalized strategy delivered through analytics can really help the clinician provide better outcomes.
Financial Risk Management - Artificial Intelligence is the key to financial risk management. According to a report by Hospitals & Health Networks, the biggest financial challenge in the fee-for-service to performance contract model is that it takes quite a lot of time in determining patient outcomes and to decide the payment. Other challenges include lower reimbursements, unpaid patient bills and underused billing and under-utilized record keeping technology. Predictive analytics can help the cash flow to the hospitals by determining the accounts that demand payment, and also predict which payments are likely to remain unpaid in the future.
Fraud & Abuse - Leveraging data and analytics can help in detecting fraud and abuse. There can be several instances of fraudulent incidents in healthcare, and it could range from honest mistakes like erroneous billings, to wasteful diagnostic tests, false claims leading to improper payments and so on. Big Data helps in identifying the patterns that lead to potential patterns of fraud and abide in healthcare insurance as well. Sniffing out false claims is no longer the tedious process it once was.
Operations - Healthcare has slowly begun to rely on technology to guide the decision making process. With improved technological infrastructure and proper analysis of data, it is possible for them to make key operational decisions. They have begun to shift away from a reactive approach to manage patient flow, making it easier to avoid operational bottlenecks and reduce clinical variations. Operational decision makers have started making informed decisions as they are able to garner powerful insights from the health system’s data.
Healthcare Reform - Through analytics, health organizations are able to drive healthcare reforms, and this will in turn drive impressive levels of reimbursement restructuring. This is powerful enough to bring about drastic changes in the present hospital-centric delivery model by making it deliver, not volume, but value, and not activity, but outcomes.
Key takeaway 2: Analytics can help you gain in-depth insights to each and every patient so caregivers can develop targeted patient engagement plans.
Big data and predictive analytics aid in making care management decisions leading to a stronger more motivational relationships between providers and patients. This plays a crucial role in generating long-term positive engagement, prevent readmission wherever possible and reduce risks of chronic diseases. By studying and analyzing structured and unstructured data it is possible for the organizations to predict illnesses, prevent epidemics and reduce mortality rates.
Combine artificial intelligence with data analysis and machine learning IoT, and it is easy to provide proactive care to patients. Hence, it would be a good move to invest in analytical solutions that can control and mitigate clinical and financial risks, with new payment bundles and models to go with it.
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