Data has been touted as the new oil, the new black gold that’s set to change sector economies and transform how organizations engage with customers, markets and solutions. Every industry is looking to mine its depths and tug out those crucial threads of insight that will allow for them to improve performance and outcomes, but the healthcare industry has specific nuances that make the data even more valuable. There is immense potential to be found in data-driven healthcare that not only streamlines data management but improves systems for patients, practitioners and institutions.
Data-driven healthcare can be broken down into three distinct pillars: the use of data by patient and practitioner; the regulation of data to ensure privacy and security; and the innovations that are driving the creation of this data.
The data-driven healthcare: access to patient data
A recent report undertaken by Pew Charitable Trusts identified the need for improvements in how patient data is used and accessed. The report highlighted how important it has become for patients to gain deeper control over their own data and for the flow of their information to become more accessible and seamless. There are challenges when patients visit different practitioners and institutions – data isn’t readily available and can even limit patient care. While there are clear privacy and data protection considerations that have to be put in place before data can be so easily moved between medical practitioners and healthcare facilities, the benefits to patient and practitioner are clear. Patients with chronic health conditions would really see the value of visible data and access as they would be able to share information, when needed, while reducing their own admin and cost burden.
In some instances, patients are expected to pay for their information or are refused access to their medical information. This puts them in a position where they have to either fork out funds for further testing or start the process over. It’s a complex situation but, ultimately, the patient is the person who creates the data so they should have the right to access that data. That said, the information held by medical institutions is often of little use to patients as they don’t have the medical knowledge to decipher the actual data. Which is why data-driven healthcare would ultimately require that data be as easy to understand for the patient as for the practitioner as this would not only improve how patients approach their own care, but how they engage with medical professionals.
This level of data sharing and insight can potentially create data ecosystems that are designed to ensure that healthcare is powered by the right information at the right time. Data ecosystems should be designed to make information usable and relevant. This can then be combined with legislation, regulation and the institution’s willingness to transform approaches to data, to define the parameters of data-driven healthcare.
Data-driven healthcare: privacy and security
Wherever there is data, there’s someone trying to gain access to that data. Cybercrime has become a phenomenal risk over the past few years and has only increased in intensity as cybercrime becomes more profitable and capable. The war for data is one of attrition, fought in regulation and legislation and on the front lines of system and security. This is further complicated within the medical sector as data here has to remain private and risk must be mitigated as effectively as possible. The GDPR in Europe set the gold standard for the protection of personal information and countries worldwide are looking to how they can follow suit. There’s a growing awareness around the management and collection of personal data and this will have a fundamental impact on the healthcare sector, particularly as it moves towards more data-driven systems and approaches.
This doesn’t mean that data-driven healthcare is hobbled before it can start. However, quite the opposite. It means that healthcare is in a position to learn from other industries how to set best practices and mistakes to create data ecosystems that are ready for regulation and capable of ensuring both privacy and security while maintaining the highest possible levels of patient care.
Data-driven healthcare: the innovation
Machine learning, automation, deep learning, artificial intelligence (AI), neural networks and intelligent algorithms – these are the drums of innovation that are beating within the healthcare industry right now. The data that these systems and solutions create is extraordinary, filling up vast virtual lakes with information that could potentially cure disease, manage treatments and improve patient care. These are equally the tools that can be used by the healthcare industry to dig deeply into this data and uncover insights that could change how a patient is treated or how a medical institution approaches patient care.
A great example of this can be found in an article published by the American Journal of Managed Care that found how machine intelligence could be used to manage outbreaks of respiratory infections. The result was that precision management through machine learning could ‘reduce the burden of outbreaks of respiratory infections’. Staying in the USA, Flatiron Health is a startup that gathers and analyzes data on cancer treatments in an attempt to refine cancer care and investment. The company pulls data from multiple sources to create insights that are of value to the industry.
There are multiple startups exploring the potential of data-driven healthcare: Aidoc in Israel, developing intelligent solutions for radiology support; Concerto HealthAI using AI to predict and manage patient outcomes; Evidation Health that uses data to determine how everyday behavior can influence health outcomes; and Excientia that uses data and AI to speed up drug discovery and development. Data-driven healthcare is squeezing into the cracks and crevices of the industry and opening up immense potential for improved health outcomes, practitioner capability and industry transformation.
