Data is giving rise to better medical practice. Here’s how.


Alexis Edwards, Associate Partner at IBM UK’s Watson Health consulting Industry Transformation Team, shares how data analytics have helped improve healthcare outcomes in the UK, US, and Singapore.

Data can save lives when it is standardised, curated, and secure. Image: Canva.

What does good healthcare mean to each stakeholder? For patients, it could mean short waiting times and positive interactions with healthcare professionals, especially when one is at their most vulnerable. For nurses, it could mean providing the best hands-on care until patients are better again. For doctors, it could mean accurate diagnoses and prescriptions that ensure patients recover quickly and fully.

But for nurse-turned-healthcare transformation specialist, Alexis Edwards, good healthcare means addressing all these needs holistically and reinventing healthcare systems through digitalisation. It means developing population healthcare strategies and recommending digitally-enabled care strategies so that patients, nurses, and doctors alike benefit from more optimised healthcare systems.

Edwards joined IBM as Associate Partner at IBM UK’s Watson Health consulting Industry Transformation Team to harness the power of data sciences, analytics, and AI to help healthcare organisations move along their digital journeys. “I’m really inspired by the profound opportunity that we have to disrupt healthcare responsibly with system modernisation and healthcare redesign, which I believe to be integral to the current and future needs of healthy populations,” she says.

Edwards speaks to GovInsider about how governments in the UK, US, and Singapore have used digital tools to improve patient experiences through more accurate and consolidated patient data.

Data saves lives

Strong data analytics platforms, coupled with consolidated patient data, can support healthcare organisations such as hospitals in meeting healthcare crises when they arise.
When the 2020 Covid-19 pandemic hit, hospitals across the globe struggled to deal with the influx of patients, many of which required urgent and immediate attention. In Ontario, Canada, North York General Hospital chose to respond to this healthcare management crisis with data analytics that provided doctors and nurses with real-time data that was easy to make sense of.

“As a member of the Incident Management Team, the information on the Covid-19 dashboard gave me real-time access to critical information such as ICU and acute care Covid-19 admissions, number of ventilated patients, and the number of tests completed at the Covid-19 Assessment Centre,” said Sandy Marangos, Clinical Director of Mental Health Program at NYGH previously.

But the hospital’s digital transformation journey did not happen overnight, or purely in response to an unforeseen and acute healthcare crisis. Instead, it began in 2017, when NYGH deployed the AI-powered IBM Cognos Analytics software to develop a real-time patient dashboard that replaced at least 100 different static reports. This enabled doctors and nurses to tell from a glance if patient vitals are not right, and deliver critical and timely responses to them.

Such foresighted investment into future-proof data management technologies put NYGH in good stead when the Covid-19 pandemic hit, potentially saving countless lives.

Elsewhere, Edwards highlights how the US’s Federal health insurance programme, Medicare, has adopted data-driven quality measures when delivering healthcare. “This includes reviewing patient-reported experiences, allowing for a feedback loop that ensures we can qualify and quantify the way that care is delivered in a patient-centred manner,” she says.

“There’s a profound need for data standardisation, curation, and consolidation – because as we know, data saves lives. It allows us to look at the bigger picture, drive automation, and personalise medicine, which is definitely something that we’ll see as a growing need in the future,” Edwards adds.

Uniting health systems

Consolidated data can support agencies in better serving the needs of patients on a population-level, as population health strategies take off around the world.

As populations age, care delivery needs to be re-organised in favour of team-based care. This means shifting much of healthcare delivery from hospitals to general practitioners that are situated within neighbourhoods, such as at polyclinics.
Decentralised healthcare shifts the focus from doctor-oriented, acute diagnoses to long-term patient monitoring by allied health professionals – a key requirement of older patients who often deal with chronic rather than acute conditions.

Home to one of the world’s rapidly ageing populations, Singapore’s Ministry of Health has announced its intention to put connectivity at the core of healthcare transformation as part of its wider healthcare strategy, Healthier SG. This means uniting patient databases across primary, secondary, and private healthcare institutions to ensure seamless patient experiences.

For this to happen, healthcare institutions can no longer operate in silos. “MOH will study how we can provide better data support for family physicians, such as giving them access to patients’ medical records and tools like clinical dashboards to better track patients’ conditions and health trends over time,” the government agency announced in March this year.

As part of this journey, the MOH Office for Healthcare Transformation has already begun delivering home-based modes of care, such as its Mobile Inpatient Care at Home programme. This is supported by rising telehealth vendors such as Speedoc, which utilises cloud-based systems and robotic process automation (RPA) to ensure that patient experiences do not dip in quality, even when delivered remotely.

In the UK’s parallel journey towards integrating its healthcare systems, the country’s National Health System has sought to link clinical datasets – from local to national level datasets, across both primary and secondary sources.

This approach to using holistic social determinants of health is exemplary, says Edwards. “Using the Indices of Multiple Deprivation which monitors income, employment, health, disability, crime, and housing factors, the NHS is able to prioritise the allocation of resources within England,” she says. Only when these needs are properly identified, can the government then rely on a broad range of community factors to improve overall healthcare outcomes.

User centricity at the core of co-creating solutions

The three key stages of the IBM Garage method are co-create, co-execute, and co-operate. Image: IBM.
Finally, Edwards emphasises the importance of user-centred design thinking, which can ensure that healthcare meets the needs of patients from the get-go. One such process is IBM’s Garage Method.

This method lies at the heart of IBM’s approach to designing solutions that truly benefit end users across industries. It involves designing technologies alongside end users, followed by multiple rounds of beta testing before scaling up and implementing them.

“Quite often, when we’re designing [healthcare] solutions, we can fall into a pattern of not really ensuring that these solutions have been tested…but it is very critical to make sure that the solutions we’re creating are meeting and exceeding the expectations of the end users,” adds Edwards.

With a firm understanding of data’s place in healthcare transformation, governments will be on the path to taking intentional and informed steps to improve overall health outcomes at the national scale.