The increasing staff shortage in aged care and the difficulties associated with employing new staff remain key challenges for the sector. According to the Committee for Economic Development of Australia (CEDA) the industry requires an additional 17,000 direct care workers each year to meet basic standards of care and currently has shortages of around 35,000 personnel.
The labour market’s unpredictability makes it vital for the aged care sector to take advantage of their existing assets to maximise efficiency and minimise the risk of losing valuable knowledge of key, long-term employees should they quit.
Will Erskine, Australian General Manager of data and analytics specialist PBT Group,
says healthcare organisations’ business rules are a major asset, but their management
is often undervalued, making many simple tasks more time-consuming and complex
“The solution is to capture corporate knowledge and business rules and store and maintain the data within a centralised repository, a low-code expert system or rules engine that integrates with your existing systems.”
Low code: this employs visual interfaces with basic logic and drag-and-drop capabilities instead of complex programming languages. That means users don’t require deep technical expertise to build and update rules. Changes can be made by any computer-literate business employee.
Expert system: this is the rules engine that stores, centralises and applies all the business rules. It becomes the source of truth and ensures rules are applied consistently.
Mr Erskine says health and aged care organisations store business rules in myriad
“Many rules exist largely in the heads of people who act on them. Another source is
rules hard-coded into disparate technology systems, including electronic medical
records and patient databases. Similar rules may exist in more than one place, but not
all variants are updated when business processes or legislation changes.”
Mr Erskine says the result can be a mishmash of rules data across an organisation with
no central repository and therefore no streamlined, consistent methodology about how
the rules are accessed and applied.
PBT Group’s solution – RulesLab – standardises rules that reside within people’s heads
and removes them from hard-coded technical products, managing them all together,
within a low-code expert system.
“Using artificial intelligence (AI), RulesLab frees medical staff from labour-intensive,
repetitive tasks, enabling greater speed, consistency and quality in the outcomes of
decision making,” Mr Erskine says.
But he emphasises that RulesLab is a decision-support mechanism, not end-to-end
automation. “Humans always remain in control.”
Mr Erskine says low-code expert systems like RulesLab employ visual interfaces with
basic logic and drag-and-drop capabilities instead of complex programming languages.
“Users don’t require deep technical expertise to build and update rules. Changes can be
made by any computer-literate employee. The rules engine becomes the source of truth
and ensures rules are applied consistently.”
A rules engine adds a layer of protection to decision-making and removes the cognitive
burden on medical staff having to remember myriad pieces of information and how
those knowledge hubs interact with other pieces of information.
For example, clinicians and aged care workers frequently manage clients’ medication
regimes. As a client information database becomes more complex, more factors affect
the potential reactions to medication combinations.
“There’s an expectation that clinicians know the ramifications of mixing different
medications, but all humans are fallible,” Mr Erskine says.
RulesLab uses AI to ensure dosages are correct and patients’ individual circumstances
considered. When patients are assessed against a standard set of rules, ambiguity and
inconsistency are removed.
RulesLab can assist in triaging patients, ensuring consistency according to the level of
care required, rather than subjective factors.
In aged care facilities, agency staff are frequently unfamiliar with patients’ regular
medication regimes. RulesLab reduces the opportunity for error.
Once rules are input to RulesLab, they are tested before real-world application and
overseen by a qualified medical practitioner before implementation.