How automation of patient data workflows is transforming primary care
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How automation of patient data workflows is transforming primary care

Galina Kustova, Healthcare Solution Adviser, Anadea and Yana Sarycheva, Marketing Researcher, Anadea
Galina Kustova, Healthcare Solution Adviser, <a href='' style='color:blue;'>Anadea</a>

Galina Kustova, Healthcare Solution Adviser, Anadea

Automation of patient data workflows has the potential to transform primary care. Healthcare at every level involves an enormous amount of manual paperwork and data entry.

Healthcare across the world is overburdened with patients, even before the impact of Coronavirus (Covid-19). As populations grow, and in many developing countries, age, healthcare systems are put under increasing strain.

Not only are many healthcare systems already facing budgetary pressures, but the burdens on them at every level (from primary care to end-of-life) are greater than they were ten years ago.

However, the changes we’ve seen over the previous decade are nothing compared to what’s coming. Healthcare needs to adapt. The sector knows it should be more efficient and cost-effective, and enhanced automation of patient data workflows is going to be transformative for patients and healthcare organizations.

How healthcare needs are changing?

As McKinsey points out in a recent report, these pressures are only going to increase over the next thirty years: “By 2050, one in four people in Europe and North America will be over the age of 65—this means the health systems will have to deal with more patients with complex needs.”

At present, healthcare is episodic and reactive. For the sake of improved patient outcomes, change is necessary, but equally, organizations can’t continue to deliver care in this same format. Care needs to shift from reactive to proactive, focusing on managing long-term and more complex requirements.

Alongside these challenges, current methods of paying for healthcare simply aren't keeping up with demand. To cope with more patients than ever before, it’s estimated that the global healthcare system needs another 40 million jobs by 2030.

According to the World Health Organization (WHO), this should include another 9.9 million physicians, nurses and midwives globally over the same period. All of this is going to cost trillions. Healthcare, and funding it adequately — in many cases, better than it’s currently funded — is going to shape national budgets and economies long after the short and medium-term impact of Covid-19 fades into memory.

Yana Sarycheva, Marketing Researcher, AnadeaWhen it comes to how medical professionals spend their working hours, it needs to be where it adds the most value — caring for patients. Doctors, nurses, and administrative staff could spend less time filling in forms or wrestling with data and spreadsheets, and more time supporting patients in the most effective ways possible.

Working with healthcare organizations, Anadea has extensive experience simplifying and automating processes and systems. We design software that aligns operational goals with patient care journeys. Our team has handled complicated systems, involving eHealth records and platforms, integrating and automating a range of features that make life easier for patients and medical professionals.

Automation, alongside artificial intelligence (AI), are seen as some of the ways forward. For the purposes of this article, we will keep our focus on automation, and in particular, how automating patient data workflows can transform primary care.

How automating patient data workflows can transform primary care?

Primary care is the main way the majority of patients start new healthcare journeys. Unless you are giving birth, need emergency treatment or end-of-life care, then most journeys start with primary care. This means, as part of national healthcare systems, millions interact with primary care services every day.

Anything that will make this process more efficient will generate massive cost savings. Patient data workflows can be automated in a number of ways, such as online forms replacing paper ones, alongside a wide range of automatic and data-driven workflow processes. We discuss those in more detail below.

In practice, enhanced and even AI-powered bot self-service diagnostics could ensure patients are signposted to the right kind of care straight away. Not everyone is going to need to see a doctor or nurse, whether virtually or in-person. A self-diagnostic system, especially if this was connected to a wearable device, would make it far easier and quicker for a patient to get the treatment they need more quickly.

An EIT Health and McKinsey & Company report found the following examples of automation in and around primary care: “apps that help patients manage their care themselves, to online symptom checkers and e-triage AI tools, to virtual agents that can carry out tasks, to a bionic pancreas to help patients with diabetes.”

Successfully designing and deploying automation systems depends on a number of factors. Creating rules, and therefore algorithms that replace human processes with automated, data-driven ones. This ensures that work can be taken out of human hands, and managed instead by software and other cloud-based systems.

When it comes to the approaches adopted, there are a number of options:

Rule-based Systems: these depend on setting up a series of “if/then/else” linear sets of instructions. This is ideal when the tasks aren't too complicated. Every solution and scenario needs to be mapped out. As scenarios change, the automated rules can be modified too, ensuring that the software is aligned with processes in a primary care practice.

Black Box: this is a well-known aspect of healthcare, especially when it comes to diagnosing illnesses. In radiology, for example, large sets of images are fed into a black-box algorithm, enabling it to recognize patterns and therefore diagnose a health issue compared to tissue or cells that are healthy. The same approach can be applied to non-health related issues. A task or question could be generated, and then a large database fed into a black box algorithm to produce an answer.

Probabilistic Networks: when it comes to this approach, humans apply patterns and interpretations, and then probabilistic network technology generates the results from the data fed into the system. Nodes in a probabilistic network represent variables of interest, with edges representing associations between them. Nodes and connections can be added, removed, and modified as the system evolves and adapts.

When it comes to automating primary care workflows, medical practices and organizations need to identify the most effective approach for their patients and processes. Once that is done, specialist IT providers can be brought into develop a solution that automates those processes, thereby making staff workloads more efficient and improving patient outcomes.

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