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APPLYING THE KONMARI METHOD TO YOUR HEALTH DATA STRATEGY

Jan Herzhoff, Managing Director for Asia Pacific, Elsevier Health

In this piece titled ‘Applying the KonMari Method to Your Health Data Strategy’, Jan Herzhoff shares some interesting parallels and lessons drawn from the popular organising consultant – Marie Kondo, whose unique way of organisation can help inspire healthcare organisations to uncover insights from the data they’ve collected and unleash the true benefits of healthcare transformation.

Marie Kondo – the pioneer of the Japanese art of decluttering and tidying up has been making waves globally. Her method, also known as KonMari, involves a mindful process of (re) organising belongings to live a more purpose-driven life. As I chanced upon her TV series recently with my family, I started seeing how her unique method can be reinterpreted and applied in the management of healthcare data, and in turn provide better care in patients’ lives.

According to IBM, it is estimated that the average person will generate more than one million gigabytes of health-related data in his or her lifetime, doubling the amount of medical data in existence every two to five years. This astonishing growth is not only contributed by the digitisation of health records, but data collected from smart sensors and wearable devices as well. Correspondingly, research from IDC predicted that healthcare data will grow to 2,314 Exabytes by 2020 from a figure of 153 Exabytes in 2013, with an annual growth rate of 48 per cent.

This poses one of the biggest challenges that healthcare providers must address: how can we organise and prepare our data in a way that achieves our ultimate purpose of raising the quality of care for patients everywhere? We all know that deep data analytics is a pre-requisite to harness the power of Machine Learning (ML) and Artificial Intelligence (AI). Yet, healthcare providers are facing a data dilemma approximately 80 per cent of this data is unstructured, which requires further review and analysis in order to unlock their value.

Therefore, while the promise of applying AI to personalise treatments and increase precision medicine is on the horizon, providers need to ensure they are capturing, sorting and uncovering actionable insights from the rich trove of data they have.

This is where the Marie Kondo philosophy comes in to provide interesting parallels that we can learn from in relation to healthcare data management.

Tip 1: Visualise Your Destination

When Marie Kondo first meets her clients, she would ask them to envision the life they aspire to live. Similarly, it is important for providers to take a step back and reflect on their vision in healthcare as well – whether it is the aspiration to improve the design and process of clinical trials, develop more personalised treatment options, or to establish consistent care guidelines.

Having a clear vision not only puts the “why” into perspective, it also helps you to avoid investing in technologies that do not solve real-world challenges and clutter your current IT infrastructure or data pool. Bear in mind that the more data that is collected, the more vigilant we must be to guard the security and privacy of those data – less is more.

Stay on track with your vision and you would be in a better position to develop a strategic framework, and decide what kind of knowledge and data systems are necessary to help you achieve those aspirations. Reach out to the right stakeholders and partners who can ask smart questions and provide a clear methodology on data collection and management.

Tip 2: Declutter and Tidy Data by Categories

Marie Kondo also has a distinct method of decluttering and tidying things up by categories, not location. During this process, you can reflect if the items still bring you happiness before you decide if you would like to keep them.

In the context of setting up a highperforming health data strategy within healthcare organisations, identifying and organising data into categories, or more generally, guiding distinctions, is key for deep data analytics and AI to take place. Providers should aim to consolidate pool of data sets according to categories, as opposed to having a large scale of data that is unstructured, heterogenous and locked up in silos where data may be available to a few clinicians but not the others.

To achieve this, the first step is to establish a framework to guide the collection of data in a streamlined and integrated manner. This includes patient-generated data that shows the health status of the patient, the information on the type of care received and medication prescribed, the various test results from different labs and more. The bottom-line is to ensure the continuous flow of necessary data collected that is not restricted by time and location, and to declutter those that are non-essentials. Tools such as clinical decision support solutions can help with the proper recording and structuring of clinical data, which can be analysed later on to produce actionable insights.

Next, we look at the standardisation of data format. Given that health data nowadays is collected through a multitude of sources such as electronic health records (EHRs), paper, hospital information system (HIS), and nonclinical data such as those from smart sensors, we need to ensure that the data collected can be shared easily and securely, in order to support workflow optimisation and system interoperability.

It is only when the groundwork of data preparation and standardisation is done, can structure and categorisation be applied. Without combining data from multiple sources and allowing data to communicate longitudinally, AI and machine learning will not be able to transform health data into actionable insights to help clinicians make more informed, personalised care decisions to improve outcomes.

A Step Closer Towards Customised Patient Care

As people around the world adopt the KonMari method to organise their households, healthcare providers can also look to it for wisdom while developing their healthcare data strategy. This includes setting a clear vision to declutter, focus and organise data in order to harness its full potential.

We should also bear in mind that this strategy should not be static, as the industry and technology systems evolve, we ought to continuously evaluate and adapt accordingly to be able to “spark joy” in patients’ lives down the road.

 

--Issue 45--

 

Author Bio

Jan Herzhoff

Jan Herzhoff is the Managing Director for Asia Pacific at Elsevier and is responsible for accelerating growth of the Education and Clinical Solutions business in the region. He joined Elsevier in 2012, where he was Head of Strategy for Health Solutions across Europe, Middle East, Africa and Asia, covering all five business segments in Clinical Solutions, Education, Health Analytics, Pharma and Medical Research.