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Big data in healthcare: advantages, disadvantages and opportunities

July 19, 2021

13 minutes read

Big data solutions are the top-notch of every industry today. The healthcare market is applying big data apps significantly faster than other industries. Big data solutions made for the healthcare market bring numerous merits for physicians and patients. Big data applications have a huge and visible potential making medical help better planned, preventative, personalized, and affordable. Smart technologies working with data analytics allow healthcare workers to forecast and avert infection outbreaks, decrease death rates, cut personal expenses, and improve patient outcomes.

To describe the most significant characteristics of big data briefly is not easy. For example, you can take 4 nouns beginning with “S”:

  • Size — to create value-based and personalized healthcare, you need customized solutions operating huge quantities of data.
  • Speed — customized software is continuously processing collected data.
  • Sources — to perform predictive analytics, customized solutions handle data from numerous sources in varied formats.
  • Staidness —  to downsize the danger of data deception, the customized ML-based solutions need well-organized and standardized data input.

Big data applications have changed the healthcare industry landscape completely. For example, electronic health records (EHR), telemedicine, medical imaging, surgery robots, etc. New digital solutions are implementing big data technologies and operating massive data input to open new horizons for the healthcare market. Predictive analytics technologies can optimize cost, lower time spent on paperwork, and initiate the creation of new big data products.

Big data apps for healthcare: most important benefits

The increase in the standard of life expectancy in the world’s population is a serious issue for today’s healthcare industry. People live longer, which requires more expensive medical care. Many countries can not afford their expensive healthcare systems anymore. The Healthcare industry today uses a lot of data that has to be operated by doctors. To analyze data is time-consuming and costly. 

Big data healthcare applications work with predictive analytics to create successful healthcare and transform medical help, making it more affordable and oriented on personal needs. Sensitive personal data for newly developed big data healthcare solutions is taken from multiple sources. For example, EHR, search engines, wearables, etc. Customized big data healthcare solutions working with predictive analytics open numerous opportunities:

  • patients are better informed about their health, diagnostics, and cure
  • physicians offer a more effectual, preventative, and individualized care plan diagnosis that can be more precise
  • diseases can be detected earlier
  • hospitals can optimize personnel staffing according to the prognosed admission rate
  • healthcare facilities can decrease unnecessary outlays and boost safety

Let us look at some examples of the most significant benefits of big data customized solutions in medicine.

More precise diagnostics and better-organized healthcare

Customized big data software helps physicians make more exact diagnoses and act faster and more effectively. The old fashion way of patient diagnostics is very time-consuming. A lot of ER visits, examinations, labs, and other procedures are needed. Doctors ask patients about the symptoms and compare them with those described in the medical literature. 

Often, doctors need a consultation of colleagues about more complicated cases. Big data analytics help save time processing big amounts of patient data, comparing them, and making suggestions about possible diagnoses. Some diagnoses may even no longer be sufficient nowadays. Big data algorithms can help detect and diagnose multiple variants of asthma, with various methods of treatment. Working with data mining techniques helps physicians develop a precise cure strategy for each patient’s unique case. Doctors who work with big data apps can notice inconspicuous symptoms at the beginning of the illness and take action immediately to prevent worsening.

Predictive medicine

Existing big data algorithms are created to help physicians with more accurate clinical diagnostics. Those solutions are capable of processing huge data quantities working with prognostic analytics, and predictive modeling techniques. Big data analytics help to identify diseases at the very beginning and suggest proper healing methods. Using big data algorithms, doctors can quickly detect patients who may develop a certain condition, prevent illnesses from becoming chronic, and forecast epidemics. For example, predictive modeling algorithms can be helpful by diagnosing diabetes or forecasting the spread of viruses to avert epidemics.

Using genomic data helps to develop a more exact vision of the further progress of diseases like cancer. Today, the sequence of the complete people’s genome costs a couple of thousand dollars. A decade ago, the price reached about $100 million. Subsequently, genomics data are constantly amassed, increasing our ability to get benefits from such data.

Effective management of population health 

Big data apps can be beneficial in many cases when used for the management of the healthcare industry. The specific goal of applying big data apps in population health management is transforming various medical institutions into high-performance workplaces, which is too difficult to achieve with traditional tools. In addition, big data solutions offer other important benefits like predictive modeling and data analytics able to forecast the expansion of infections and the consequences.

Prognostic analytics can help healthcare institutions substantially cut costs and boost patient outcomes by avoiding unnecessary tests and procedures or using algorithms to forecast the expected quantity of patients and prepare them with an appropriate amount of healthcare workers. In addition, some systems collect data from revenue cycle software and billing systems to aggregate cost-related data and define other areas for demotion.

Challenges of applying big data in the healthcare industry

Customized solutions using big data have significantly modified the healthcare industry by helping avoid illnesses, predict medical outcomes, downsize medical errors, and boost healthcare in all aspects: from quality to affordability. However, some structural challenges hinder healthcare facilities in applying big data into everyday operations. First of all, certain technical skills are required but not always available in healthcare facilities to manage a big data environment.

Therefore, it is a serious challenge in the process of applying big data in the healthcare industry field. Another big challenge for using big data in the healthcare industry is data safety. Big data storages are attractive for hackers. It is a reason why most healthcare organizations protect sensitive personal data. All healthcare apps must meet the requirements for data security and be HIPAA compliant. Let’s think about several other common drawbacks for the mass applying of big data apps developed for healthcare workers:

Quality of input data: integration, standardization, processing, and sharing

Customized big data solutions are capable of operating significant data quantities but have certain requirements. Input data needs to be standardized, unified, free of duplicates or mistakes. Data are often taken from diverse sources and are differently formatted. It makes operating this sensitive personal medical data challenging. For successful integration and processing, data must be well organized and standardized. Working with not qualitative data might cause diagnostic errors or the wrong cure.

The correctness of data input is challenging but mandatory, it belongs to the top priority area while used for developing the data management healthcare apps. Big data algorithms have to work with adequately organized data to avoid errors in the field and achieve the most impressive patient outcome. While gathering and sharing sensitive patients’ data, providers have to be in touch with each other and share only qualitative and properly organized data. Only standardized, unified, and tested data has to be implemented into the system. All prepared data has to be automatically checked and tested regularly.

The robust data privacy protection is another challenging but important issue while sharing sensitive healthcare data with various healthcare providers. Big data solutions created for medical data management must be HIPAA compliant.

Visualize sensitive data

When speaking of intelligent technologies and medicine, the most essential are solutions for constant monitoring of patient data, tracking data from wearables, organizing amassed data, and data visualization on dashboards, and in the created reports. Visualization is challenging but crucial for creating images, diagrams, or animations to communicate medical messages and enhance understanding. In addition, specific personalized healthcare applications, management, and customized analytics software must be developed to visualize sensitive data and resolve safety and scalability problems properly.

Practical examples of implementing big data solutions for the healthcare industry

Personal digital healthcare records

Implementing Electronic Health Records (EHRs) is the first and solemn step for constant data tracking and analytics to boost healthcare quality. 

Data contained in the EHR belongs to the essential sources for creating customized data tracking apps. Many healthcare facilities have already applied EHRs to let healthcare workers view the complete healthcare history of each person: demographics, chronic illnesses, performed lab, and other tests, prescribed medications, and specific information about lifestyle – this and other data is saved in the EHR. Healthcare workers from different facilities can share and edit data in the EHR records to add data and suggestions to achieve better cure results.

Medical data exchange, data sharing, and assistance in the decision-making process are now possible without needless doctor appointments, additional labs, tests, and other procedures. Big data apps allow avoiding data replication and immense paperwork. All systems used for sensitive personal data exchange must be HIPAA compliant and safe from data violation. Sharing the confidential data contained in EHRs can significantly enhance healthcare and result in better-tailored healing. Healthcare workers can now see side-by-side data of various people having similar conditions, analyzing generic components, and personal habits such as smoking, alcohol consumption, quality of sleep, and others.

EHRs often offer opportunities to activate alerts and reminders for patients to follow doctor’s instructions correctly, take prescribed medications, and not miss ER Visits or lab tests. Implementing EHRs can significantly improve existing treatment protocols helping prevent illnesses, epidemics, decrease mortality rates from heart attacks and other chronic diseases. EHRs help optimizes expenses by avoiding needless ER Visits, tests, or other procedures.

The strategy of preventing diseases is more effective than healing. Big data-based healthcare apps help providers, doctors, and care receivers to focus on preventive medicine. Doctors care about the benefit of patients staying healthy and provide them with advice about a healthier lifestyle after analyzing the data contained in EHR. Big data solutions help with prognostic analytics, assisting in diagnosing illnesses in the initial phases. To predict further illness and receive warning signals timely, doctors must get acquainted with intelligent technological inventions, which can be a great help in healing people. 

Alerts 

Many big data healthcare applications have alerting functionality implemented, which is a very useful invention. However, alarm signals generated by big data healthcare apps that constantly monitor personal medical data may endanger a patient’s life. In particular, reminding a diabetic person to take prescribed medication or eat on time may help somebody not fall into a diabetic coma. In such situations, people need a wearable device to constantly monitor their sugar levels. In addition, such wearable devices are very useful for people with a tendency to have dangerously high blood pressure. If the blood pressure data is constantly monitored, the doctor can be alerted and may help the patient remotely. 

Alerts help not only in urgent situations as described above, but can also remind patients about the necessity to follow precisely the doctor’s instructions, helping to avoid needless doctor appointments, labs, and other procedures. In addition, alerts help to correct prescriptive decisions. For example, the wearable devices that constantly monitor asthma patients are the helping hand for doctors by prescribing proper healing and avoiding the condition worsening.

Sensitive individual health data may be gathered continuously by wearables and saved in external storage. Thus, physicians can compare data analytics from various persons with similar conditions and correct the planned treatment accordingly. If better-targeted healthcare service is a goal, data analytics helps a lot. Customized big data healthcare applications working with predictive analytics offer alert functionality as well.

Applications that work with prognostic modeling data can help in avoiding epidemics. For example, wireless devices that constantly monitor and gather the data of proper handwashing in certain institutions like hospitals or other healthcare facilities can assist with the data helping predict infection spreading.  

Risk management 

Customized big data-based healthcare solutions show good results in predictive analytics helping healthcare facilities significantly boost the level of medical care. Prognostic ML- algorithms can help doctors develop precise and patient-targeted strategies for medical help. Especially patients with complicated diagnoses and various chronic conditions can use new big data technologies in healthcare. Conditions at the early stages can be detected easier with the assistance of big data apps. In addition, big data enables more effective healing prescriptions for care receivers, insurance companies can better calculate possible risks, hospitals can cut unnecessary costs.

Using predictive analytics, big data healthcare algorithms can assist healthcare facilities in better planning of personnel staffing according to the expected admission rates. For example, knowing the approximate number of expected daily patients, managers can prepare by arranging the proper care and downsizing expenses.

Customized big data solutions created for healthcare may track data of people with a high risk of unexpected hospitalization, helping to avoid such hospitalizations. Alerts are very helpful in those cases.

Analyzing various patient data like prescribed pharmaceuticals, individual symptoms, lab results, the number of doctor appointments, and other data makes it possible to focus on appropriate preventative care and reduce unnecessary hospital admissions. Big data solutions can assist in risk calculation and cost downsizing, for example, in the case of home treatments. Big data solutions allow healthcare facilities to manage risks effectively, optimize the allocation of resources, and offer special care for the most needy patients. Institutions, which apply big data healthcare solutions, boost patient outcomes and are better financially well-organized.

Medical Imaging 

Healthcare workers have to examine and keep a lot of medical images needed for a proper diagnosis. To do it manually in the traditional way takes a lot of time and is expensive. For example, radiologists must analyze each CT, MRI, or PET image separately, and healthcare facilities have to store it. Big data-based healthcare solutions rationalize the analysis of medical images.

Big data algorithms for healthcare processes, and compare big quantities of visual data to convert them into digits and detect certain patterns in pixels, helping physicians create history catalogs of images. Computer vision and data science techniques are very helpful for picture analytics, improving the diagnostic results. Big data enables doctors to analyze and compare images identifying the smallest changes and suggesting possible diagnoses.

Telemedicine 

The term telemedicine has existed for several decades. Telehealth technologies work for distant medical care. The popularity of big data-based telehealth apps has increased rapidly recently, not least because of the pandemic. Remote medical consultations, wearable devices giving the doctors and other healthcare workers, opportunity for remote patient monitoring, electronic prescribing, additional schooling for doctors, and other services are top-notch for new big data-based healthcare solutions.

Remote surgery technologies enable surgeons to supervise surgeries performed by robots. As a result, doctors don’t even need to be physically present in the clinic during the surgery.

Customized big data software for telemedicine allows more individual healing strategies and is focused on preventive medicine. For example, taking action on time allows achieving fewer hospitalizations or readmissions in some specific chronic cases.

In conclusion let us summarize some benefits of big data apps for telemedicine and the healthcare industry in general:

  • Patients no longer have to wait in lines for ER Visits.
  • Doctors have fewer needless examinations and desk work.
  • Doctors can monitor and consult patients remotely.
  • Chronic patients with complex medical diagnoses can be saved from unexpected hospitalizations and readmissions.
  • Healthcare workers receive alerts about possible deterioration cases or acute medical events and can take action on time.
  • Healthcare facilities can cut expenses while boosting the level of healthcare services.
  • Medical help becomes more available for all categories of patients.

Conclusion

All of this implies that big data offers a variety of services and advantages to the healthcare sector, ranging from early disease detection to selecting the optimal treatment plans based on clinical and genomic data. Big data initiatives have the potential to alter the healthcare system in the near future.

VITech works with a variety of healthcare companies to assist them to manage their data assets using medical big data analytics. We leverage big data in healthcare software to implement technologies that can help any size healthcare company simplify and enhance efficiency. Need assistance with a digital healthcare solution? Contact us at:  info@vitechteam.com

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