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Predictive analytics in healthcare: benefits and challenges

May 13, 2022

7 minutes read

Modern technologies, including Big Data and machine learning, have opened new horizons for the predictive analytics and decision support systems market. With proper use, they will be the next step in the digital transformation of any industry, including healthcare.

Today, the market for predictive analytics is exceptionally fast-growing. According to a study by AMR, from 2020 to 2027, the cumulative average annual growth of the predictive analytics market will be 21.9%, and the yearly turnover of the predictive analytics market will reach $35.45 billion by 2027.

Benefits of predictive analytics in healthcare

There are several ways the benefits of predictive analytics in healthcare can be applied. Let’s consider the main ones.

Episode analysis

The transition to value-based payments is just around the corner. Given the need for such changes, medical institutions are under unprecedented pressure to optimize costs and constantly improve the quality of services. Who will be the champion in this race? Organizations use big data to analyze performance, identify optimization opportunities, and reimagine healthcare delivery.

Population health analysis

The benefits of predictive analytics in healthcare using big data and analytics to manage public health is an issue that is being actively discussed in the industry today. However, it remains to be understood and decided how to establish an effective monitoring process.

Virtual medicine and patient-wearable medical devices

Technology enables healthcare providers to implement virtual patient care solutions that improve the quality of care and increase access to services. However, the question remains: how safe is an ecosystem that accumulates more confidential information about people’s health?

Project data area

It plays a crucial role in increasing pharmaceutical data transparency across the industry. The goal is to create a secure data processing and analysis environment accessible anywhere, within which medical institutions will be able to exchange anonymized information about clinical trials. This will help scientists quickly process research results, which, in turn, will improve the quality of patient care faster.

Modern systems of predictive analytics in healthcare are often built as cloud web services that can accept an anonymized electronic medical record or a separate medical document as query parameters. Let’s see how it can work.

After the system receives all the information, it is subjected to format-logical control to have a certain level of trust. Then an essential thing begins: the system uses various algorithms to identify suspicions of a disease, even if the doctor did not establish it in a clinical diagnosis, but the machine sees that, most likely, there is a disease.

If the doctor confirms the disease, the system predicts how the disease can develop shortly. Risk factors are identified, scales, models based on machine learning, and digitized clinical recommendations are applied.

Related: Predictive analytics in healthcare: importance and future

Advantages and disadvantages of predictive analytics in healthcare

Big data and predictive analytics in healthcare – following areas:

  • predicting the development of diseases,
  • identifying genetic markers in oncology,
  • predicting the health status of infants,
  • making diagnoses using wearable devices.

Big Data analytics in medicine combines analysis methods from several scientific fields, such as bioinformatics, medical imaging, sensory and medical informatics, artificial intelligence, etc. Big Data technologies aim to analyze increasingly complex arrays of medical data formed from diverse sources of information in structure, format, and reliability. Big Data includes both structured data and unstructured and semi-structured data (XML documents).

Thus, Big Data in medicine is continuously and rapidly replenished electronic arrays of vast volumes of medical and paramedical data that are qualitatively different from each other and cannot be managed using traditional tools and software and/or hardware. The introduction of Big Data technologies will solve current problems in medicine and provide an opportunity to overcome the unattainable horizons of medical data processing in healthcare, leading to predictive analytics in healthcare. At the same time, this study cannot be considered complete; it is necessary to continue work on the study of Big Data technologies and algorithmic solutions for processing big data. Studying the features and prospects for the use of Big Data technologies in medicine and healthcare is an urgent scientific and practical task.

Thus, if we talk about the advantages and disadvantages of predictive analytics, it is worth highlighting its colossal potential. But its implementation is hampered by high cost and the need to use the most complex technologies. However, this investment will definitely pay off: in the future, predictive analytics in healthcare will be a significant cost savings factor.

Predictive analytics for cost-effectiveness in healthcare

The example of the United States can easily explain the need to improve the efficiency of health care. The top 10 drugs most commonly prescribed by Medicare help only 21% of patients taking those drugs. In other words, 79% of prescriptions are wasted.

Likewise, only five out of 10,000 women over 50 who have a mammogram every ten years avoid death from breast cancer. Not only that, 6,000 of them get false-positive results.

These are examples of impersonal health care or “inaccuracies in medicine.” The fact is that in modern medicine, standard methods of treatment are used for all patients. The disadvantage of this approach is not only in financial losses — it is a matter of life and death.

Today, the United States is the world leader in developing and implementing predictive analytics in healthcare. The primary basis for their development is the economic efficiency of their implementation. According to McKinsey Global Institute analysts, the use of Big Data technologies in US healthcare will generate a financial flow of $ 300 billion in value terms, of which two-thirds will come from reducing the costs of the US healthcare system.

Related: How is predictive analytics used in healthcare?

It should be noted that today Big Data is not only information but also a tool that has unlimited possibilities and contributes to obtaining new solutions, a qualitative transformation of medical care processes, and the progressive development of the healthcare system as a whole. Scientists are investigating how big data can be used to improve evidence and clinical decision-making. Big Data in medicine and healthcare allows an entirely new approach to the provision of medical care, both concerning one person and considering the state of affairs on a city or even country scale. Currently, Big Data technologies are used for:

  • improving decision making in clinical practice;
  • identification of patients at risk who have experienced adverse reactions or established ineffectiveness of drugs;
  • improving the quality of individual patient care to obtain better results;
  • improving the indicators of the proven effectiveness of drugs, taking into account the data obtained during actual practice;
  • improving health resource planning.

The great potential of using Big Data technologies in medicine is associated with developing algorithms for recognition, further analysis, and interpretation of signals and images. The signals from wearable devices, which are characterized by a large volume and speed of arrival, mainly when used continuously in real-time, generated by many sensors connected to the patient, are tough to process, store and analyze.

Thanks to advances in the field of Big Data, it is possible:

  • to fully implement a personalized approach to the patient and his medical history;
  • predict the development of the disease and the risks of treatment;
  • determine the signs of the disease, patterns of its course, and therapy;
  • monitor the patient’s condition using various devices;
  • evaluate the effectiveness of therapy and identify links between the causes of the disease and its symptoms;
  • predict more accurately calculate the need for equipment and drug provision;
  • manage patient flows, and much more.

The digitalization of medicine is a promising direction that unifies the work of clinics or laboratories and can also save human lives. In 2016, the digital healthcare market was valued at $179.6 billion, according to Transparency Market Research. According to the forecasts of the analytical agency, GARP in this segment will be 13.4% until 2025, when its volume will exceed $536 billion.

In digital medicine, including in world-class clinics and research centers, there are still many gaps and vulnerable areas. But practice shows that the technologization of even those processes that at first glance are not amenable to it, as well as building a coherent system for collecting and exchanging information today, help to find growth points that will determine what healthcare will be like in the foreseeable future.

Predictive analytics in healthcare is an exemplary process of research and analysis of a large amount of complex heterogeneous data of different nature: biomedical, electronic medical records, pharmaceutical, legal, insurance, publications in social networks, and others. The introduction of Big Data technologies will lead to the development of optimal ways to process, analyze and extract practical knowledge from these large volumes of medical data.

As a result, predictive healthcare will improve the quality of patient care, diagnose various pathologies at an early stage, predict and prevent the development of diseases, and the occurrence of epidemics, provide the most effective personalized treatment methods, and control the quality of medical institutions. Contact us to find out how you can use the benefits of predictive medicine today.

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