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How to cure the system with data analytics

The pandemic harms not only people but also the entire healthcare industry. The Covid-19 outbreak seriously affected the system, exposing several loopholes in need of urgent patching. According to the State of the US Health System report, disparities in healthcare that are associated with particular demographic groups, lack of crucial data on social determinants, delayed care, and other issues recently became even more vivid.

So how do we fix the situation? Addressing the challenges effectively calls for a comprehensive approach that requires specific efforts from policymakers, industry experts, and healthcare providers.

Provider’s input into solving the global crisis

The bulk of target efforts against disparity are required from the providers’ side. This is where health data analytics can come to the rescue, facilitating and simplifying gathering large volumes of social determinants of health (SDOH) and other data, processing it correctly, and visualizing the results.

To evaluate the state of vulnerable patient groups in their clinic, providers need to collect SDOH and the data on their effect on diverse groups of at-risk populations. To understand why care gets delayed in their organization, providers can then measure the effectiveness of the established workflows, personnel, and medical and educational campaigns running in the area using data analytics tools. Examining the resulting insights, analysts then can suggest ways to reform the existing processes and draft new targeted outreach programs to meet patients’ needs and improve health outcomes.

Let’s take a closer look at how health data analytics can help address all three major healthcare disparity reasons: the lack or improper usage of SDOH, demographic-based imbalance of services, and delayed care.

Extracting data from unstructured information

It’s nearly impossible to manually extract and organize all of the patient’s health data coming from multiple sources. Some pieces will get lost, duplicated, misplaced, or put in incorrectly. And if we are talking about thousands of patients, the load of such manual work is onerous for the medical personnel. As a result, the healthcare organization’s system is missing vital information about the health and living conditions of patients.

Trained with machine learning (ML) algorithms, data extraction, and equipped with artificial intelligence (AI), analytical solutions can process huge volumes of unstructured information: documentation in different formats, even handwritten, medical images, conversation records, etc. This way, healthcare organizations will have access to already organized accurate information about their patients and be able to use it for patients’ advantage.

Improving population health and preventing inequality

After extracting data from disparate sources, including research databases and demographic data sources, healthcare analytic software helps detect and report cohort patterns to evaluate the overall state of wellness in a particular group of patients that may be living in a particular area, belonging to a vulnerable ethnic group, exercising a specific lifestyle among other factors. Using predictive analytics, it’s possible to foretell the consequences of a lifestyle or living conditions on a group’s health, set up epidemiological alerts, improve educational campaigns, and take other actions to boost population health.

In addition to that, a whole set of challenges related to vulnerable patient groups can be solved. These include transportation challenges, life necessities, and insufficient income. Designing outreach techniques to solve these challenges is likely to make a difference for any healthcare provider. For instance, by applying data analytics to patients’ SDOH, providers can find low-income patients and tip them on where they can purchase generic alternatives to costly drugs.

Monitoring the organization’s performance and enhancing care

Custom-made medical analytics software allows for monitoring operations, employee efficiency, facility performance, and more in real-time. It also helps find the correlation between the established workflows and health outcomes for the patients. Consequently, organizations can redesign some of their processes, redistribute finances, resources, and personnel to where they’ll be more effective, and upgrade health services to fit their current needs in the particular area. These actions contribute to overcoming care delays:

  1. Compensating for the staff shortages by pointing out routines that can be automated, tasks that can be performed in a more efficient way, and ways to redistribute the workforce across one organization.
  2. Taking pressure off the ER and hospital personnel by enabling earlier diagnostics and alerts about possible complications to prevent critical worsening of the conditions which would require admissions to the ER. The same is true about hospitalizations in general – with better preventive care the number of in-patients can be significantly lower, which decreases the waiting time for those who need hospitalization and also improves in-hospital care.
  3. Acquiring more financing by making operations more transparent and outcomes more measurable. Investors prefer to put their money towards clear objectives where one can understand the reasons for success or failure. Analytics can visualize the financial and operational flows of healthcare organizations to make them easier to understand.

The real-life case: decreasing hospitalization length with smart analytics

Amidst the pandemic, a hospital in Pueblo, Colorado had to join efforts with another local care facility. However, when the partner shut many of their units, the hospital had to onboard large volumes of patients. However, most in-patients had an excessive length of stay, which prevented the hospitalization of new ones.

To solve the challenge, the hospital leveraged a new AI-driven tool that looked at unstructured data and identified factors hampering patient discharge. Then the system created a discharge checklist for doctors, which included impeding factors for each patient and helped clinicians address them.

The new tool allowed the hospital to reduce the length of stay by 88%. In the meantime, it also helped them achieve a number of positive changes: in-patients’ concerns started to be addressed quicker, patient satisfaction and loyalty increased as they received more personalized pre-discharge care, hospitalization happens faster and more patients can be admitted. .

summing up

Though the pandemic hasn’t subsided yet, some efforts to address key system loopholes and their detrimental effects can be implemented. The ‘disaster recovery’ in this case needs to include a comprehensive approach that implies effective measures at governmental and local levels.

The latter depends on providers in particular and whether they will take action to tackle health disparities faced by their patients. Collecting SDOH, adding this data to patient profiles, enabling health data analytics, and taking data-based actions has proven to help significantly. With joint effort, government organizations, healthcare professionals, and data analysis solution providers can help improve patient outcomes and population health both locally and nationwide.

Photo: goir, Getty Images

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