Big Data Analytics of Healthcare that Saves Lives

Big Data

Big data has revolutionized how we manage, analyze, and utilize data in many industries, including healthcare. Healthcare analytics can have a great effect on reducing treatment costs, anticipating epidemics, and averting preventable diseases. Overall increasing the quality of life.  Since human lifespans are growing worldwide, professionals in the medical field must collect vast amounts of data. Find the best strategies to use it. This article will discuss why big data is essential in healthcare and how it helps patients. Lastly, we will give 21 examples of how big data is used to support medical-based institutions. Let’s first review the concept of big data in healthcare.

What Is Big Data In Healthcare?

  • Big data in healthcare is a term used for large sets of information created by the use of technology that collects patient records. They can be effectively managed and measured.
  • The application of big data analytics has numerous benefits that can potentially save lives. It refers to vast amounts of information generated by digitization which is then consolidated and analyzed using special technologies. Applied to healthcare, this can help to prevent disease, reduce costs, etc.
  • Currently, with life expectancy increasing, approaches to treatment have changed and much of this change is driven by data. Doctors want to be able to understand a person as fully and early in life as possible. Early indications of severe illness can be spotted and treated effectively and cheaply. Utilizing healthcare indicators and analytics, prevention is key: it allows insurance companies to tailor packages for individuals. One issue in this area is that a patient’s data can reside in siloes. Stored in different places like hospitals, clinics, and surgeries. Which makes it difficult for professionals to access it.
  • However, the sources of patient data are on the rise. portals, studies, EHRs, wearables, search engines, standard databases, and government agencies. Also payer records, staffing schedules, and even patient waiting rooms. For years collecting huge amounts of healthcare data was both expensive and time-consuming. Improved technology makes it easier to gather such data while creating comprehensive reports. It can then be transformed into useful insights. This is why we use healthcare analytics. So issues can be identified quickly before it’s too late. Treatments are evaluated more rapidly while keeping better track of inventory.
  • Now that you’ve grasped the importance of big data in healthcare. Let’s investigate 21 real-world cases that show how a data-driven approach can improve processes, and care for patients. Most importantly, save lives.

1) Patient Forecasting for Improved Staffing:

  • As any shift manager knows, it’s vital to get staffing right. Having too many workers will ramp up labor costs while too few could lead to inadequate service. A potentially life-threatening situation in healthcare!

2) Electronic Health Records (EHRs):

  • This is unquestionably the most common use of big data in medicine. Every patient has their own digital file that includes personal details, medical history, allergies, laboratory results, etc.. Which can be shared via secure systems from both public and private providers.
  • They also feature a single modifiable file to save on paperwork and avoid data duplication. Additionally, EHRs provide warnings or reminders when a patient should have a new test or check. If they’re taking medication correctly.
  • The US has made great progress with 94% of hospitals adopting EHRs according to this HITECH research; however, Europe lags behind. But an ambitious directive by the European Commission is trying to change that.

3) Real-Time Alerts:

  • Clinics employ Clinical Decision Support (CDS) systems that quickly analyze medical data and offer prescriptive advice for health practitioners.
  • To reduce in-house treatments, healthcare professionals are looking to use wearables that monitor patients’ health data and send it to the cloud. Similarly, the socio-economic context can be used to modify delivery strategies.
  • Additionally, sophisticated tools can continuously monitor this massive stream of data. Send an alarm if the results turn out to be abnormal. An alert is triggered if a patient’s blood pressure increases dramatically.

4) Patient Involvement:

  • consumers are now interested in using smart devices that monitor their daily activities. Vital signs; allows them to identify potential risks. Patients are directly involved in monitoring their health and receive incentives from insurance companies to lead healthy lifestyles

5) Preventing Opioid Abuse in the US:

  • As of this year, opioid misuse has resulted in more accidental deaths in the US than in traffic accidents, making it a very serious issue. Canada even declared opioid abuse a national health crisis, and former President Obama allocated $1.1 billion dollars to find solutions.
  • Big data analytics may just be the answer we need. Data scientists and analysts at Blue Cross Blue Shield. Fuzzy Logix has identified 742 risk factors that can predict with high accuracy if someone is at risk for opioid abuse.
  • Although helping those deemed ‘high risk’ is an intricate task, this project gives us hope for lessening the impact of this issue on people’s lives and the system as a whole.

6) Using Health Data For Strategic Planning:

  • The utilization of big data in healthcare enables strategic planning because of a better understanding of people’s motivations. Care managers can review check-up results among different demographic groups to identify what keeps them from getting treatment.
  • The University of Florida used Google Maps and free public health data to make heat maps for multiple topics such as population growth. Then compared them with medical services accessible in those same areas.

7) Big Data Could Possibly Be a Cancer Cure:

  • The Cancer Moonshot program was proposed by President Obama during his second term. Aimed to make 10 years of progress in curing cancer in just half that time.
  • Can be used to identify trends in real-world treatments and patient recovery rates. Can even uncover unexpected solutions – such as Desipramine’s ability to help cure certain types of lung cancer.
  • To achieve this, patient databases must be linked up from different sources like hospitals, universities, and nonprofits. It is not without its challenges: incompatible data systems and patient confidentiality laws must be addressed

8) Predictive Analytics In Healthcare:

  • Predictive analytics in healthcare is an important trend that is helping doctors make data-driven decisions quickly. Which is particularly useful in cases of complex medical histories.
  • With this, healthcare organizations can also predict who may be at risk of illnesses like diabetes and provide extra screenings or weight management plans.

9) Reduce Fraud And Enhance Security:

  • Reducing fraud and improving security is also a major focus. Studies show that 93% of healthcare organizations have experienced a data breach, so many are now utilizing analytics to spot changes in network traffic and other suspicious behaviors to prevent threats.
  • To address the inherent security issues of big data, organizations have equipped themselves with encryption technology, firewalls, and anti-virus software. Other solutions for optimum protection. This helps streamline insurance claims processing and Allows patients to get better returns on their claims as well as quicker payment for caregivers.”

10) Telemedicine:

  • Telemedicine has been around for over 40 years, but with the development of online video conferences and smart devices, it has been able to fully blossom. This term refers to the use of technology for providing remote clinical services.
  • It is used for primary consultations, initial diagnosis, remote patient monitoring, medical education, and even telesurgery. Meaning doctors can perform surgery remotely with the help of robots and real-time data delivery.
  • Telemedicine also allows clinicians to provide tailored treatment plans to prevent hospitalization or re-admission through healthcare data analytics – like predictive analytics that can anticipate acute medical events in advance.

11) Integrating Big-Style Data With Medical Imaging:

  • Every year in the US approximately 600 million imaging procedures are carried out, which is extremely time and money-consuming due to manual analysis and long-term storage of images.
  • Carestream explains how big data analytics could be utilized to identify patterns within an image’s pixels and turn them into a number that would aid diagnosis – thereby radiologists may not have to look at images in the future but instead analyze the outcomes of algorithms that can remember more images than they could ever do manually
  •  This would inevitably have a big influence on radiologists’ roles, education, and skill set.

12) A Strategy To Reduce Unnecessary ER Visits:

  • Have you heard about the Oakland woman who visited the ER 900 times in three years? Her issues were made worse due to the lack of medical record sharing among hospitals, increasing both costs for taxpayers and difficulty for her to get good care. Alta Bates Summit Medical Center’s Tracy Schrider said it was a mess of wasted resources for both hospitals and the patient. To prevent cases like this from happening again, Alameda county Hospital created PreManage ED – a program that shares patient records between emergency departments. This enables ER staff to access tests done at other hospitals, case manager assignments, and advice given to the patient – all in an effort to maintain a coherent message. Healthcare analytics is necessary and this is a great example of how they can be used.

13) Smart Staffing & Personnel Management:

  • Without engaged and unified personnel, patient care will suffer, service rates will decline, and mistakes will occur. To overcome this, healthcare organizations can utilize big data tools to streamline staff management in various ways.
  • By utilizing the right HR analytics, medical institutions can maximize their staffing while forecasting the demands of operating rooms, thus improving patient care.
  • Often in healthcare institutions, the staff is distributed unevenly which can lead to a lack of motivation in the workforce and higher rates of absenteeism.
  • With data-driven analytics though, it’s possible to predict when personnel is needed in certain departments during peak times and equally distribute skilled personnel throughout the institution during lower traffic periods.
  • Moreover, analytics allow senior staff or executives to provide adequate support when needed, plan strategically, and make the staff processes as efficient as possible.

14) Learning & Development

  • Continuing on from the last point; having skilled and confident people in hospitals or medical institutions is necessary for positive outcomes.
  • While doctors are highly knowledgeable in their fields of expertise, there are also other positions within one roof such as porters and admin clerks as well as cardiac specialists and brain surgeons
  • Not only do these people need certifications but they must have soft skills too to keep the institution running at its full potential. To do so you must promote continuous learning and development for all staff members.

15) Advanced Risk & Disease Control

  • Using big data and healthcare analytics, we can easily identify and better predict the risks and health of patients with chronic illnesses. This helps us reduce hospitalizations by finding the right medical treatment, tracking symptoms, monitoring the frequency of medical visits, and more.
  • This then translates to lower financial costs for healthcare institutions, as well as more resources being available for those who need them most. Ultimately, using data-driven insights in healthcare can work wonders to both save lives and improve patient care overall. 

16) Prevention of suicide and self-injury:

  • Nearly 800,000 people die from suicide each year, and 17% of the world’s population experience self-harm at some point in their lives. These numbers are staggering, which is why big data applications in healthcare are making a positive difference in suicide and self-harm prevention.
  • Hospitals and other healthcare institutions regularly monitor patients’ EHR data, combined with a standard depression questionnaire, to identify those at an increased risk for attempting or succeeding in suicide.
  • Using a predictive algorithm, researchers found that those within the top 1% of flagged patients were 200 times more likely to attempt or succeed in suicide. As former Gartner VP and research director Paolo Magrassi once said: “If someone pushes the data enough (openly or not), it will confess anything.”

17) More effective supply chain management

  • When a medical organization’s supply chain is weak or fragmented, it can have a huge impact on patient care, finances, and so forth. That’s why big data in healthcare is so invaluable when it comes to supply chain management; it helps us track performance metrics, make smarter decisions regarding expenditure, and optimize ordering processes – all resulting in an estimated saving of up to $10 million per year for hospitals.
  • Using descriptive and predictive analytics models allows us to better negotiate prices and reduce variations in supplies while ensuring treatment is administered quickly without any costly delays.

18) Exploring New Therapeutic Avenues & Innovations:

  • Our healthcare analytics next example focuses on creating a better future for the medical industry. Big data analysis in healthcare has the potential to assist in discovering new treatments and innovative drugs.
  • By combining historical, real-time, and predictive metrics along with effective data visualization techniques, healthcare professionals can identify potential strengths and weaknesses in trials or processes.
  • Furthermore, through analyzing genetic information from data-driven sources and predictive reactions in patients, big data analytics in healthcare could be of major importance for the development of groundbreaking new medications and advanced therapies.
  • Data analytics in healthcare can help to streamline operations, innovate approaches, ensure safety, and even save lives. It provides certainty and insight, paving the way forward.

19) Aiding With Mass Disease Management & Tracking:

  • Since the beginning of 2020, the COVID-19 pandemic has had an immense effect on people all around the world. The widespread nature of this disease presented a challenge to health professionals who found themselves trying to learn from it while also trying to control it simultaneously.
  • In this regard, big data played an essential role in helping the global response against this virus that brought life to a standstill for years.
  • With the aid of advanced data management technologies, health experts have been able to track. Monitor how COVID spreads dynamically; how it mutates depending on different factors; as well as its effects on different world economies. All through analyzing massive datasets from sources such as individual patient records and human behaviors.

20) Processes toward better drug prescriptions:

  • As we have seen with all the cases mentioned, the huge amount of data available to healthcare professionals has been a game-changer for patient care, disease prevention, and many other areas.
  • So it is no wonder that this trend has made its way to the pharmaceutical industry. Where it can maximize drug values and improve prescription quality. This brings us to our next big-data healthcare application.
  • One company that has embraced this is Express Scripts, an organization that manages medicine coverage for customers who provide health insurance plans. According to a Pharmaceutical Journal report, the US-based firm collected information from 83 million patients containing both clinical. Behavioral aspects like somebody getting a prescription or Tweets directed at them. With this data, they are able to foresee critical scenarios such as who is likely to become reliant on particular medications or those not following their treatment plan.

21) Eliminate Human Error:

  • Healthcare fraud ranges anywhere from inaccurate billings to inefficiencies leading to unnecessary tests. The incorrect medical records are being entered
  • The National Health Care Anti-Fraud Association estimates financial losses due to healthcare fraud in the US alone may reach $300 billion annually.
  • This implies roughly 10% of total health spending is lost due to fraud or human error. However, money isn’t even the main issue here. If given the wrong medicine or treatment can put people’s lives at risk Leading to possible long-term effects or death.

Overall, these 21 examples of healthcare analytics have revealed three major trends. The patient experience will drastically enhance, including the quality of care and level of acceptance. Auch on a sustainable basis can improve the general health of the population. Significantly decreased Operational costs

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