Access to the right data, and the technology to leverage it effectively, will also unlock greater healthcare value. By understanding the unique journey of each patient, from diagnosis to the efficacy of specific treatments, and management of any adverse effects, healthcare professionals can make informed decisions that can ultimately lead to better outcomes.

However, there are barriers to achieving this intelligent, connected approach in healthcare. For starters, data is fragmented, which makes it difficult to deliver efficient, personalised care. Meanwhile, growing compliance and cybersecurity challenges require healthcare organisations to be proactive in the way they approach governance.

Piecing together the data puzzle

The healthcare industry faces unique challenges when it comes to data. Traditionally, healthcare institutions have bought and operated their own systems, with disparate patient records left scattered across different providers and databases. Patient data becomes fragmented, within organisations and the industry at large. As with any silos, this breeds inefficiency and makes it difficult to use data both to serve the needs of the individual patient and society. When this data is completely anonymised and used in aggregate, it is invaluable for larger community and even worldwide analysis, diagnosis, research, and action.

To unlock the potential of health data, the traditionally well-managed structured data needs to be brought together with unstructured data to create a single view across a range of data sets. Healthcare organisations are just scratching the surface when it comes to collecting data from an ever-increasing range of sources. Data from telematics, wearables, and patient apps are often stored in massive data lakes, which amounts to looking for a needle in a haystack with it comes to finding key insights. Moving to an autonomous database in the cloud enables providers to use their data to its full potential and deliver insights that improve patient care.

Powered by Oracle Analytics Cloud and Autonomous Data Warehouse, Sejong Hospital, the only cardiac speciality hospital in South Korea, has transformed the lives of over 1,600 children suffering from cardiac disease. The collection of data throughout the medical process and seamless delivery of real-time information to medical teams means that lifesaving decisions that used to take hours can now be made in minutes.

For American Hospital Dubai, their major digital transformation also aims to deliver better patient outcomes using data. Partnering with Cerner and Oracle, the hospital implemented a new electronic health record system to help physicians deliver a better patient journey, as well as introducing a resource planning platform to help it reduce costs and enhance productivity. This integrated patient data also drives the hospital’s AI and robotics research work, leading to further patient benefits.

Healthcare is not one size fits all

No two patients are the same. They have different healthcare needs, treatment plans, and contact preferences. And yet most patient care remains standardised. The pandemic has driven increased patient expectations, with the rise of virtual appointments and mobile health alerts increasing the range of personal experiences patients receive. Indeed, telemedicine platforms alone have seen a 1,000% growth rate. If healthcare providers double down on personalised patient experiences, online and offline, then everyone gets the right support for them. In addition, outcomes can potentially be improved by ensuring better individual patient adherence to treatment plans.

Implemented effectively, a rigorous approach to data management can deliver greater personalisation and lower costs. Coloplast A/S, a Danish multinational that develops and manufactures medical devices, empowers patients with a personalised support programme. Built on Oracle Eloqua, Coloplast Care supplements the help patients receive from nurses and doctors, providing them with information and support that is personal to them. This kind of support can minimise the risk of preventable conditions, improving patient care and reducing pressure on healthcare services.

Working within the confines of regulations

The majority of consumers worry about the security of their health data, and personal healthcare records are rightly subject to protections. Providers can work with regulations, leveraging data to deliver better patient outcomes in a compliant, secure manner. These systems run in line with regulations such as the GDPR include categorisation and safeguards specific to health data. They also include data localisation requirements, which are especially sensitive for cloud-based healthcare providers. This makes robust governance paramount, not just to protect patients, but also to protect organisations from prosecution.

Dutch health insurer Zorg & Zekerheid ensures its customers get high quality, affordable care using rich data sets, implementing automation to increase data security. Implementing Oracle Autonomous Data Warehouse has automated almost all manual tasks that can cause human error, providing optimum security, cutting costs, and saving time. Data is secured with encryption of unused and active data, protection of regulated data, and rapid auditing and threat detection. This protects the organisation and its data against breaches, malware, persistent threats, and account hijacking.

Data-driven healthcare

We have seen that the healthcare providers which get their data management in order deliver better patient care and gain a competitive advantage. Connected and secure data will not only drive improvements within healthcare institutions – it will feed into broader medical advancements, increasing diversity and efficiency in clinical trials, which can result in getting new treatments to market faster. The responsible use of healthcare data can save lives, and we have only just begun to scratch the surface of what can be achieved. As Dr Shetty from Narayana Health, the world’s largest heart hospital, says “we believe in God, but for everything else we need data”.

By Karen Senior, Strategic Lead, NHS at Oracle

The post To Deliver Better Patient Outcomes, Healthcare Providers need to Refine their Approach to Data appeared first on .

Hospitals now have an average of 10 to 15 internet-connected devices per bed. Medical device manufacturers are increasingly experimenting with data-collecting “smart” wearables and similar technology.

In practice, this data is already being used by hospitals and health care startups to improve inventory management, or offer more personalized health care to patients. It’s likely, however, that the medical field isn’t leveraging the full potential of this medical data.

Medical data analytics is already important to the industry — but it’s likely to become much more central over the next few years. This will likely create both major opportunities and new challenges for health care providers.

How Medical Data Analytics Is Used Right Now

Major health care providers and manufacturers are also beginning to use predictive analytics to help manage demand forecasting and inventory management during periods of supply chain instability.

With historical sales data or hospital inventory data, for example, it’s possible to better estimate resource requirements of essential medical supplies and treatments. This helps health care providers and manufacturers manage periods of unstable supply and demand.

This information, derived from solutions like barcode scanners for hospitals, can also help health care facilities improve the visibility of their inventory by creating real-time, digital records of facility medicine and equipment.

This can allow a nurse, for example, to know exactly how much of a particular kind of PPE a hospital has in store, as well as where that PPE is being stored. With this information, both individual employees and the organization itself can respond faster in a crisis.

In an interview with the Economic Times of India, Charlie Farah, the director of industry solutions at Qlik, described how this approach has worked in practice.

A particular application of data analytics can allow “hospital staff [and] managers to identify stock levels of PPE by location — with this information, management can relocate available stock to staff caring for patients that have fever or respiratory symptoms at other locations.”

Medical facilities are also utilizing predictive analytics to reduce patient wait times and more effectively manage hospital staffing. In the near future, these applications of predictive analytics may become more widespread.

Future Applications of Healthcare Data Analytics

As healthcare data analytics becomes more sophisticated — and as more aggregated health data becomes available — more advanced and patient-specific applications of medical data analytics could become commonplace.

One change will likely be the more widespread use of medical data in personalizing health care. With analysis of a client’s particular health data, an algorithm may be able to pinpoint treatments that are more likely to work than others — reducing the need for a trial-and-error approach when more than one treatment option is a possibility.

Chronic traumatic encephalopathy (CTE), for example, is an extremely common condition in post-career athletes, like football players. Despite its prevalence, however, there is no cure or treatment for the condition. The condition’s cognitive and behavioral symptoms can be treated, however — but the effectiveness of treatments for these symptoms can vary significantly from patient to patient.

Analysis of medical data like a patient’s health history, bloodwork, and genetics can help providers better understand which treatment options may work best — giving them somewhere to start.

In an article for Health IT Outcomes, Dr. Joost Huiskens of SAS Netherlands predicts “more and more health care providers will become data-driven organizations” in the years following the end of the COVID-19 pandemic.

Huiskens argues that the future of the health care industry depends on the adoption of personalized care, and the use of AI analytics and vast amounts of health care data to tailor treatments to each patient’s particular biology.

Information like a patient’s DNA is already being leveraged by medical startups to provide unique health care insights by a number of startups.

Soon, hospitals and other major care providers may also begin to more frequently use DNA health analysis as a first-line approach in treatment, helping doctors to more effectively pursue the most effective therapy possible for a particular condition.

However, it may be some time before major health care organizations are able or willing to adopt this tech. Before hospitals can begin offering these kinds of services at scale, it could be misleading to say that medical data analytics has become fully realized.

Challenges for Medical Data

The growing use and storage of health data also presents significant challenges that health care providers will need to manage. Hospitals have already become an increasingly popular target for hackers and cybercriminals.

As the amount of stored medical data grows, cyberattacks may become more common.

Finding ways to de-identify aggregated health data — which would make that data less valuable to hackers — while improving facility cyber defenses will likely be necessary before medical data analytics can become fully realized.

Barriers and Challenges for Medical Data Analytics

As medical data becomes more widely available, medical data analytics will become more essential to the health care industry. However, it’s likely that medical data analytics won’t be used to its full potential in the near future.

New applications of medical analytics — like DNA analysis for personalized health care — aren’t commonplace yet. Hospitals that adopt this technology may also have to manage the growing threat of medical data breaches.

Article by Shannon Flynn – Rehack

The post When Will Medical Data Analytics Become Fully Realized? appeared first on .