How Big Data is Changing the Healthcare Landscape

Big data has been making big changes in hundreds of industries for years now and there is no question how important it is in data collection and solutions execution in our modern lives. Many businesses use big data as a tool to enhance their marketing and advertising strategies and to build customer relationships by providing real time solutions and personalised engagement.

The buzz that big data has generated over the last few years is hardly surprising. Just imagine the sheer scale of data that is being produced every day. While some industries are falling behind and missing the opportunity to harness value from large-scale analysis and integration of big data, healthcare is just getting started by leveraging data and predictive analytics.

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Value In Healthcare

Past healthcare systems and data models were based on negative and reactive models as compared to positive and predictive models. To keep on track with worldwide changes to how we handle data, healthcare systems are slowly undergoing a change to holistic and patient-centered framework which will allow for a redesign of how healthcare is carried out. Big data and healthcare systems could mean:

  • The right care. This ensures the patients gets the most appropriate and timely treatment available. It also relies on protocol and a coordinated approach between providers, caregivers and other health professionals for a consistent and competent treatment of a patient.
  • The right provider. All patients should be treated by top notch medical professionals that are best matched to the task at hand and will give the best results. It means that the right doctor and staff should be assigned to a patient according to their conditions and needs.
  • The right value. Use of big data could improve quality of service in the healthcare industry. This involves ensuring cost-effective care, tying provider reimbursement to patients results or outcomes, eliminating fraud or preventing abuse in the system.
  • The right innovation. This involves the identification of new approaches and therapies and encouragement of R&D.

Why Is Big Data Essential In Health Care?

Many healthcare organisations around the world have large quantities of data available and a large portion of it is unstructured or clinically relevant. More and more healthcare organisations are leveraging big data to capture consistent patient information. The primary of this technology is to get better insights that can help and aid diagnosis and treatments of patients. Harnessing big data can help in achieving 3 important objectives of healthcare:

  1. Build sustainable healthcare systems. The healthcare industry is forever faced with pressure from competition and legislation and must always determine ways of reducing costs of care while effectively managing resources.
  2. Increase health care access. One of the major issues of healthcare is access and for the population to thrive, health care must be readily available, accessible and of course affordable. Educating people on preventive care can help enhance health, reduce the demand and waste of healthcare resources.
  3. Collaborate to improve care. All healthcare organisations should have personalised healthcare initiatives that will improve the quality and efficiency of care. By understanding individual patients, healthcare organisations will be able to design effective health care programs for individuals.

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Healthcare organisations are seeing the value that comes with investing in applications and other analytics tools in helping healthcare stakeholders to identify the value and opportunities of big data. Most modern healthcare organisations are using two types of data sets:

  1. Retrospective data – which is basic event-based information that is collected from medical records and  real-time clinical information acquired and presented at the point of care.
  2. Real-time clinical data – information that is captured and presented at the point of care, for example, getting the blood pressure, heart rate, imaging, oxygen saturation and other tests. Let’s say for example, a diabetic patient is admitted to a hospital and complains about numbness in their toes, instead of assuming that the cause of the numbness is diabetes, the clinician will monitor their oxygen saturation and blood flow and determine if there’s something that is more threatening like stroke or possibly aneurism.

Many pioneering technologies have already succeeded in putting these two data types into action so that clinicians can acquire relevant information and use it to identify trends that will impact the future of healthcare, which is also known as predictive analytics.

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Reasons Why Healthcare Big Data Is Unique

Many people who work with data tend to think of it in very linear and structured terms, but data for healthcare is not made that way. It is complex and diverse, making many traditional or linear analysis inapplicable. There are different characteristics which make healthcare data unique.

Most of the data stored for the healthcare industry is stored in multiple places like HR software, EMRs, and different department systems like radiology or pharmacy departments. Collecting all the data into a single and central system like an enterprise data warehouse will make information more accessible and more actionable. Many electronic medical record software provides a platform for consistent capture of data, but in reality data capture is not consistent. Electronic medical records were used to try to standardise the data capture process within the healthcare industry, but not everyone has been convinced on adopting a one-size-fits-all approach, thus much of the data is hard to aggregate and analyse in a consistent manner.

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Changing regulatory requirements will also affect how data is captured in the industry. As these requirements continue to evolve, content management systems require quality reports around measures and the information on pricing for the public. This shift to a value-based purchasing models will add to data complexity for healthcare organisations.

Data from health care will get simpler in the future as some experts believe. It will grow and the healthcare industry will face unique challenges and with that, it will also encounter unique data challenges. And because healthcare data is complex, a different approach should be applied, which can handle the multiple resources, including the unstructured and structured data and format inconsistency issues in an ever-evolving environment. The solution for this is to have an agile and fine-tuned approach to healthcare.

Healthcare Is Ready For Big Data

Below are some of the essential reasons why healthcare truly needs big data:

1. Data Integration

Businesses and political campaigns have linked disparate data sources to learn everything possible about clients, customers and citizens. They apply advanced computation and analysis in modifying existing strategies or creating new ones. It also leverages heterogeneous data and securely links them to identify the right treatment for a specific patient or group of patients. The lack of standardisation in health care data is a big challenge. Data integration will require leadership and collaboration from the private and public sector.

2. Generating New Knowledge

One of the earliest uses of big data was to generate new insights which also resulted in predictive analytics. In addition to clinical and administrative data, integrating additional information regarding the patient and their environment will provide enhanced prediction and provide the right interventions to the right patients. The collected predictions can help in identifying areas to enhance the efficiency and quality in healthcare. These areas might include readmissions, treatment optimisation, any adverse events and early detection of conditions.

Many hospitals are beginning to use graph analytics to evaluate the relationship across complex variables like lab results, nursing notes, diagnoses, patient’s family history, medications and surgery in identifying patients who are at high risks of an adverse outcome. Efficient assessment and better knowledge of facts about patients at risks could be the difference between a timely medical intervention and a missed window of treatment.

3. Translating Knowledge Into Practice

The new analytical methods and standardised data collection are critical in big data, but practical application is the key to success. This is a challenge for those who generate and those who consume the new knowledge. Users like doctors, patients and the policy makers need to be engaged at the beginning. The research team should also have a good understanding about how to translate the new knowledge into actual practice.

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Smart Big Data In Healthcare

Most healthcare providers usually ignore about 90% of the data they generate, but in recent years they have learnt to take advantage of the information they have. Those who have started harnessing big volumes of data and combining them with the new patient-generated information will fast track their adaptability in the face of major healthcare industry challenges – increasing costs, payment shifts and growing chronic diseases. It has been estimated that taking advantage of big data will give a saving of up to 17% in healthcare costs. Healthcare providers implementing best practices will surely help in streamlining coding, billing and managing supplies. It will also allow small and large healthcare providers to access and analyse the outcomes of their patient, which will show them where to spend and where they can save funds.

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Big data can also help healthcare providers in evaluating their practices and comparing them to other healthcare providers. Having evidence-based practices at the organisational level will create opportunities for the healthcare industry to thrive.

Big data is here to stay and the healthcare industry should take advantage of it.


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