Healthcare, like many other sectors, is moving through technological change. All of the technologies in development need patient data. The healthcare sector already collects masses of information, but much of this is unstructured and not curated. As healthcare expands its data collection capacity, what does this mean for privacy, security and patient care?
The healthcare sector generates masses of data. However, much of this information is not structured and not applied to practical patient solutions. Gartner points out the need to unlock what can be hidden value in the healthcare data being collected.
According to Gartner “Using cloud-based AI, healthcare providers can predict upcoming patient needs and identify optimum interventions, before the patient’s condition starts deteriorating. Cloud-based as-a-service solutions have become the preferred strategy for CIOs who need to innovate while controlling costs by converting capital expenditure outlays to operating expenditures.”
Dr Anurag Gupta, research vice president at Gartner, also commented: “Cost optimization is a consistently recurring theme among healthcare providers. The money that RPA saves by not having to spend as much on an unreformed process translates into cash that is available for front-end clinical functions, which is especially important. At the same time, healthcare organizations combat the COVID-19 crisis.”
Indeed, according to research from EY the estimate the value of curated NHS data could be upwards of £5 billion a year, delivering around £4.6 billion in benefits to patients.
The key is to place the myriad of data points within the context of the patient and the stated goals for the data being analyzed. In their report, The Health Foundation concludes: “Support for clinical decision-making is one area where new technologies are changing quickly, as evidenced by the proliferation of tools and algorithms to help clinicians diagnose and manage the disease. Development is rapid in both the public and private sectors, and in health care, the digital future seems rich with possibility. As Robert Wachter noted, ‘Big Data techniques will guide the treatment of individual patients, as well as the best ways to organize our systems of care.’”
The most current high-profile application of technology to healthcare is the COVID-19 tracing app. Trialled in the Ise of Wight, for a national rollout, the app was abandoned in favour of app systems developed by Apple and Google.
Also, the app has come under increasing criticism for their lack of universal tracking. Indeed, the lack of evidence supporting the effectiveness of an app to trace anyone who has come into contact with a COVID-19 individual is conspicuous by its absence. The UK’s government is still developing some form of the app. Whether this sees the light of day remains to be seen.
Privacy and security have been highlighted continuously as aspects of healthcare data collection that must always be addressed as new applications and devices are developed. The Academy of Medical Sciences comments: “There is evidence that, while patients and the public recognize the benefits of data-driven technologies, these should not be at the expense of their access to healthcare, their privacy or their rights to make personal choices about their health and lifestyles. If properly deployed, these technologies can enhance patient choice and the potential suite of healthcare options, while also protecting their autonomy. In this chapter, we set out some principles that reflect public and patient views on privacy and rights.”
Speaking to Silicon UK, Camilla Winlo, Director of Consultancy at DQM GRC, explained how healthcare data must be paired with robust personal information security. “Track and trace apps and immunity passports both by definition, contain health information about individuals. This is considered ‘special category’ data under the GDPR because of the risk of discrimination. In this case, there is the risk that people who have or have not had COVID-19 will be treated differently because of their health status. This is potentially harmful; an example could be that immune people might receive preferential treatment at work, and those who are not are disadvantaged as a result.
“Track and trace apps have also opened up the discussion about data privacy. The first responsibility of any data processing operation is to achieve its stated goal, which is quite tricky for track and trace apps. Simple mathematics tells us that to achieve its target of being downloaded by 60% of the UK population, the NHSX app would have to be the most successful app ever. Based on precedent from other countries, this is unlikely.
“There are other concerns too: The ICO indicated that a decentralized model was preferable, but the government decided to build a centralized model instead, raising questions about the ICO’s role and influence. The government also failed to publish a DPIA before the app was launched, which is a breach of the GDPR. Both of these suggested that privacy was not a primary concern for the developers.”
Winlo concluded: “Perhaps the most significant impact is that we have been able to see, in some detail, why transparency and accountability are such fundamental principles within the GDPR. Without trust, people simply won’t allow their data to be processed – even by the NHS, and even for as significant a reason as managing a public health crisis.”
Personalizing the healthcare of an individual is the Holy grail of all healthcare systems. Speaking to Silicon UK, Pamela Spence, EY Global Health Sciences and Wellness Industry Leader said: “Unlocking the power of health care data to fuel innovation in medical research and improve patient care is at the heart of today’s health care revolution. A data-centric approach to health care promises to deliver interventions more proactively. This will lead to better healthcare outcomes, with a shift towards prevention rather than treatment, and a higher degree of personalization.”
Spence concluded: “Knowing a patient’s data at a particular time – and how it compares to other people – is often more important than longitudinal information about that particular person. For example, the current healthcare issue facing a patient could be unrelated to their health a year earlier. As such, the key to unlocking greater personalized healthcare is the analysis of curatable datasets to identify patterns and make the most informed treatment decisions.”
The Academy of Medical Sciences describes: “There are already several projects underway that are exploring the potential of in-home monitoring to improve care and patient outcomes. For example, the Sensor Platform for Healthcare in a Residential Environment (SPHERE) and Technology Integrated Health Management (TIHM) for dementia. SPHERE employs a variety of sensors in the home to monitor and record health-related behaviours.
“These projects have incorporated public and patient dialogue to help make the monitoring and surveillance technologies acceptable to participants. They highlight the increasing understanding across all sectors of the importance of involving patients and the public in discussions around the use of data-driven technologies.”
As the medical technology (MedTech) sector matures and expands, what was focused on fitness will expand into other more diverse areas of patient care and wellness, as EY’s Pamela Spence continued: “There are already many MedTech companies, but the critical aspect to their longevity rests with their value proposition. Entrants from the tech sector that have deep customer engagement and advanced data and analytics skills see the health space as a fertile area for new growth.
“Moreover, there are clear signs that leading tech companies are moving beyond fitness and wellness tracking to care management, using easy-to-use consumer-facing devices. Some are also developing data-rich platforms that make it easy to share data proactively with consumers and providers to avoid adverse health events and optimize care management at the individual level.
“These new entrants have considerable firepower to do deals if they want to accelerate their health activities,” Spence concluded. “This will be a major advantage as companies seek to assemble the breadth of talent, technology and expertise needed to take the next steps toward personalized health care.”
Mimi Keshani, Head of Operations and IT Evangelist at Hadean and a visiting scientist at the Francis Crick Institute warns:“While we know that treatments designed for individuals rather than a one-size-fits-all approach lead to better patient outcomes, the promise of personalized medicine has yet to be realized. We are collecting and analyzing more data than ever before, and powerful computing power is now at our fingertips – but operating at scale is still a challenge. However, current infrastructure was not designed for the sheer volume of this data and, harnessing the potential of cloud computing requires a redesign of the tech stack to eliminate operational bottlenecks.”
Also, Ric Thompson, Managing Director of Health and Care at Advanced points out that collecting healthcare data is all well and good. Still, technology developers must not forget the individual and how they must be engaged with the technology their personal data is powering.
“For healthcare organizations to reap the rewards of data analysis, there are several barriers to driving digital transformation that need to be addressed: From ensuring there is the investment and resources to drive the technology agenda safely and securely, to ensuring patient care and patient data integrity is at the heart of any change.”
Thompson expanded: “But one of the real concerns is in the ability to drive change in the way people think about technology and, ensure uptake in new ways of working to deliver the efficiencies and clinical benefits of data-driven care. Many of the senior leaders in the NHS understand the need to advance the transformation process, recognizing it is key to delivering a better healthcare fit for the future. This is about ensuring people understand the vision – which must be that technology can deliver the most valuable resource – freeing up valuable clinical time away by taking away administrative and time-consuming processes to focus on patient care.”
Healthcare, in general, is increasingly relied upon the collection of data to deliver tangible benefits to patients. The thousands of datapoints that are already being collated to form a digital profile of an individual will expand almost exponentially over the next few years.
The analysis from EY concludes: “Unlocking the power of health care data to fuel innovation in medical research and improve patient care is at the heart of today’s health care revolution. When curated or consolidated into a single longitudinal data set, patient-level records will trace a complete story of a patient’s health, wellness, diagnosis, treatments, medical procedures and outcomes. Health care providers need to recognize patient data for what it is: a valuable intangible asset desired by multiple stakeholders, a treasure trove of information.”
Collecting data for its own sake has never delivered any insights or practical outcomes in any industry or sector. How that information is utilized is the key to unlocking its potential, especially when health care is considered. The public and private sectors understand that their focus must be towards effective therapies and treatments for patients and, how data can be used for preventative medicine.
Silicon in Focus
Vladimir Tkachenko, General Manager at Amaxa Pharma.
Vladimir Tkachenko – a pharmaceutical sciences expert – is general manager of Amaxa Pharma, a UK-headquartered pharmaceutical company focused on life-threatening therapeutic areas such as oncology and neonatology.
Is the healthcare sector entering a new era where personal data becomes the basis of treatments and preventative care?
Data is the basis of treatment. All protocols for the treatment of various diseases are based on processing a huge amount of data, stemming from clinical studies of new molecules, and this is especially true for the likes of international multi-centre studies.
Further, when the safety and efficacy of a new drug are shown, it starts to be used in clinics to treat patients. However, research continues in various clinics around the world, and practitioners compare this drug with other medicines. These are independent studies, and very often, researchers in one country do not know that such a review has already been or is being conducted in another country. This is precisely why processing a considerable amount of data helps to develop the best recommendations and protocols for the treatment of specific diseases. Big data and correct interpretations allow us to talk about evidence-based medicine, constructed on facts.
Longitudinal data across healthcare services are often fragmented. How can technology help us curate and integrate that data into meaningful datasets?
Indeed, longitudinal data across healthcare services are often fragmented. There is indeed a lot of data, and it is very difficult – often impossible – to determine the degree of reliability and integrate this data into meaningful data sets.
I believe we need to work on the criteria for including data in an integrated sample, as well as improving standards for these criteria, and sometimes developing standards for processing this data. Artificial Intelligence (AI) technologies that can determine the reliability and significance of information but with specific criteria already exist.
For example, my company works with one laboratory in the United States; we send a patient’s tumour sample to this laboratory, and we conduct an analysis that allows us to select the chemotherapy drugs that are most suitable for the effective treatment of cancer in a particular individual patient.
This is a fairly standard approach to the choice of treatment tactics for a particular patient. So, the laboratory team analyzes biomarkers and compares them with an existing database, and then generates them into a report providing data on biomarkers, possible treatment and links to clinical literature. This is a huge database of biomarkers and available data from clinical trials that are being processed.
During the decision making, treatment protocols for a particular nosology are adopted. So, the patient has prescribed treatment and the result of this therapy is known by their doctor, who, depending on where they work, in which country and in which clinic, enters data on the results of treatment into the appropriate database. The question is how to access these databases, considering ethical, legal aspects. This is a very difficult issue, which if and when it is resolved in the future, using new technologies for data collection and processing, we can improve significant datasets.
Do we have the analytical capability to extract meaningful data from each patient dataset without bias or discrimination?
Technologically, we do have an opportunity to extract meaningful data from each patient dataset without bias or discrimination. Moreover, within the framework of closed systems, for example, a single hospital – this is already happening.
However, when there is a need to combine data from different systems, then, of course, we are faced with the need to obtain permissions from the patient to collect and distribute the information. Usually, this is a rather tricky and financially expensive process. Also, claims of bias or discrimination may arise from how the data is interpreted. To avoid this, you need to create very strict criteria for the inclusion of data and its processing.
Healthcare organizations can collect masses of data, but is the real challenge of how this information is interpreted? Is this where AI will deliver the tools need to use the data available to provide real patient benefit?
Healthcare organizations can indeed collect a lot of data. They already do this and have been doing it for a long time, long before the introduction of computers. The question is how to validate this data because the quality of interpretation will depend on this. Modern CRM systems do not solve this issue, and AI technologies can help in this. However, we are faced with another problem, as we need to obtain the data from various hospitals located in different regions, and this is a much more significant limitation than we imagine, firstly with regards to ethical and legal norms.
As FinTech businesses are transforming financial services, will we see a raft of new start-ups to create a HealthTech sector driven by data and analytical technologies? Where do you think the next major innovation in the health tech sector will come from?
Healthcare is a rather difficult industry to manage, which, in addition to the traditional understanding, also includes political, environmental and socio-legal aspects. Big Data consumers in healthcare can be represented by the ministry of health and local governments. This is precisely what we see today regarding immunity passports or a programme for tracking the movement of at-risk people.
However, if we are talking about prevention, diagnosis, treatment and rehabilitation of patients, then the emergence of HealthTech is much higher because it does not depend so much on government orders and the needs of the political leadership of a country – although there are some limitations. Now there is a need for high-quality and affordable diagnostics. Moreover, just in the diagnosis, there is a certain global consensus on how to receive data, how to analyze it and how to interpret it. I think that just in the field of diagnostics, breakthrough HealthTech start-ups may appear.
When we talk about treatment approaches, we must understand that this is a very, very conservative industry. Still, at the same time, existing start-ups will allow us to determine the treatment strategy for this or that patient, considering the obtained analysis data and their interpretation.
Another area is professional platforms where doctors of various specialities can exchange experiences and find answers to difficult questions. Still, from my point of view, this project may be limited due to an incomprehensible monetization scheme.
Clinical trials of new molecules are another industry in which new technology can significantly reduce the cost and speed up the process of launching new drugs. For example, we are currently seeing that just the duration and regulation of clinical trials significantly affect the launch of the vaccine against COVID-19.
Therefore, I think that significant innovations will appear in the field of diagnostics and monitoring of human organs and systems. In the near future, we will see start-ups that will fragmentarily solve certain health problems, but it is unlikely that they will change the healthcare sector as rapidly as FinTech did for financial services.