Looking back on events that have unfolded over the past two and a half years, it is safe to say that several areas have undergone tectonic transformation. Health is one of these sectors that has been highlighted, due to its importance in managing the COVID-19 pandemic. A subsector of the healthcare industry, durable medical equipment (DME), has traditionally lacked streamlined inventory and ordering systems. The industry was also plagued with time-consuming functions, which sometimes led to errors in order processing and delivery.
When the pandemic and subsequent lockdowns drove a digital transformation of operations, the EMR industry was quick to embrace the technology. The rapid proliferation of digital technologies in operations has resulted in the generation of tons of data. This data can present a set of opportunities for companies not just in the DME sector, but across industries. It was at this point that professionals looked for solutions that would help them learn from this data, and artificial intelligence (AI) emerged as a winning solution. Validating this, a recent survey from IBM and Morning Consult stated that nine out of ten IT workers in India use 20 or more different data sources to inform their AI, BI and analytics systems in their companies. .
Read between data
Ensuring that an organization makes full use of its AI platform requires a strong data strategy. As Google notes, an “AI system is best understood by the underlying data training process, as well as the resulting AI model.” . Equally important is having the skills to meticulously examine the data.
Paying close attention to the data used to train the AI model is one of the easiest ways to improve its explainability. Teams must decide where the data will come from to train an algorithm, whether it was obtained legally and ethically, whether the data contains biases, and what can be done to reduce them during the design phase. Given that 67% of companies leverage more than 20 different data sources for their AI, this is a significant task that should not be underestimated.
Understand the relevance of data
Moreover, it is crucial to carefully remove data that will not be essential to the result or that would not be relevant. A better way to ensure that no irrelevant factors affect the inputs to the algorithm is to not include the data in the training set.
Companies run into problems using AI because they lack data, their staff lack the required technical know-how, and they cannot trust or understand the decisions made by AI.
Data-Enhancing AI Capabilities
A robust data strategy is advised to identify the different types of data needed to meet the business challenge and improve their solutions. Such an approach is necessary to get the most out of AI, including structured and unstructured, internal and external, qualitative and quantitative data. Permission-based governance that demonstrates data provenance should come next to increase trust in data and AI results. Finally, one must be prepared for the difficulties of meticulous data analysis, combining various data sources and using the appropriate tools.
Coming back to the DME sector, using AI to decipher data can benefit operations through increased order fulfillment, ultimately leading to revenue growth. At the same time, players can also automate workflows, to connect healthcare actors with EMR providers. These are models that can be replicated across industries, to bring efficiency and effortless operation.
In conclusion, adopting an effective data management and AI deployment strategy goes hand in hand, because without the right tools it is impossible to leverage data across the enterprise.
The opinions expressed above are those of the author.
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