{"id":9611,"date":"2021-07-10T21:32:59","date_gmt":"2021-07-10T19:32:59","guid":{"rendered":"https:\/\/www.essenburgh.com\/how-to-reduce-workload-in-healthcare-with-smart-algorithms\/"},"modified":"2021-07-31T19:39:31","modified_gmt":"2021-07-31T17:39:31","slug":"how-to-reduce-workload-in-healthcare-with-smart-algorithms","status":"publish","type":"post","link":"https:\/\/www.essenburgh.com\/en\/blog\/risk-stratification-drive-value-based-healthcare-and-the-triple-aim-with-smart-algorithms","title":{"rendered":"How to reduce workload in healthcare with smart algorithms"},"content":{"rendered":"

How to reduce workload in healthcare with smart algorithms<\/span><\/h2>\n

Consultation hours are overflowing and much of the time is spent ticking off mandatory lists. It’s driving caregivers crazy! Understandable, but not necessary. <\/b>With a different focus and approach, there is a lot to gain in terms of time, money and satisfaction. The secret lies in looking at risks rather than diseases and using their own data intelligently with the help of algorithms. In this blog we describe how you can organize your care based on patient groups with a low, increased, and high risk profile. We show that this leads to: 1) better outcomes for patients, 2) lower work pressure and greater job satisfaction for caregivers and 3) more efficient operations and cost reduction for healthcare administrators. And the best part is: it’s within easy reach.<\/b><\/p>\n

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Focus on risk, not disease<\/strong><\/h3>\n

The major pitfall of our current healthcare system is the focus on disease. These days, we are dealing with a growing group of people with two or more illnesses at the same time, who are at risk of falling between two stools because of this focus in terms of care. It is much smarter to coordinate and organise the provision of care on the basis of risks and the associated health needs of patients. By identifying unique subgroups of patients, healthcare organizations can provide the right care tailored to them rather than ‘one size fits all’<\/em>. Thus, they have more influence on better outcomes for these patient groups. In addition, they can organize care more efficiently and cost-effectively. This means that the traditional classification (diabetes, COPD, heart failure, oncology, frail elderly) of groups will become obsolete. This is a new way of thinking, at least for the Netherlands. In other countries, such as America, Australia, New Zealand and Colombia<\/a> it has already proven its effect. Best practices there show that realizing the right care in the right place starts with segmenting patients based on risk stratification models.<\/p>\n

How does risk stratification work?<\/h3>\n

All healthcare providers must record data. GPs do this in the GP information system (HIS) and hospitals use a hospital information system (HIS). By applying algorithms to the data in such a system, patients can be categorised fairly easily on the basis of risk profiles (see Figure 1). Some people have trouble using algorithms because of negative associations. This is unfortunate and unjustified, because algorithms can detect patterns much better than an average healthcare provider, and<\/a>this will benefit your organisation enormously. What exactly, we will try to explain by means of three risk profiles that can be detected by means of risk stratification. We will briefly outline the profiles and then indicate step-by-step how you, as a care provider\/institution, can go about tailoring your care provision to these profiles.<\/p>\n

Figure 1:<\/strong> How risk stratification works<\/p>\n

\"Better<\/p>\n

High risk patients<\/h3>\n

This group, which represents approximately 5% of the population, includes people who, for various reasons, visit the family doctor and\/or another healthcare provider more than twenty times a year. The superusers <\/em>, so to speak. As expected, the frail elderly are well represented in this group. We also find the Wajongers with mental health problems there. But the group is more heterogeneous than we think. Risk analyses show that middle-aged women with families are also superusers<\/em>. A group that does not easily attract attention, but – just like the other high-risk patients – can cause clogging up of the consultation hour in daily practice.<\/p>\n