The epidemic can harm not only people but the entire healthcare industry. The Covid-19 outbreak had a serious impact on the system, exposing a number of loopholes that required emergency patching. According to a State of the US Health System report, inequalities in health care associated with specific demographic groups, lack of critical information on social determinants, delayed care and other issues have become more pronounced recently.
So how do we fix the situation? It calls for a comprehensive approach that requires specific efforts by policymakers, industry experts and health care providers to effectively address the challenges.
Supplier input on solving a global crisis
More targeted efforts against inequality are needed on the part of suppliers. This is where health data analytics can come to the rescue, facilitate and simplify the large-scale collection of social determinants of health (SDOH) and other data, properly process it and visualize results.
To evaluate the status of vulnerable patient groups in their clinic, providers need to collect data on SDOH and their impact on diverse groups of at-risk populations. To understand why care is delayed in their organization, providers can then measure the effectiveness of established workflows, staff and medical and educational campaigns in the area using data analytics tools. Examining the resulting insights, analysts can then suggest ways to improve existing processes and create new targeted outreach programs to meet patients’ needs and improve health outcomes.
Let’s take a closer look at how health data analysis can help address all three major health disparities: lack or misuse of SDOH, population-based imbalance of services, and delayed care.
Extracting data from unstructured data
It is almost impossible to manually extract and organize all the patient’s health data from multiple sources. Some pieces are lost, copied, strayed or misplaced. And if we are talking about thousands of patients, such a manual workload is heavy for medical staff. As a result, the healthcare organization system has lost important information about the health and living conditions of patients.
Analytical solutions trained with machine learning (ML) algorithms, data extraction and artificial intelligence (AI) can process large volumes of unstructured information: recording, handwriting, medical images, conversational documents, etc. In this way, healthcare organizations have access to already organized and accurate information about their patients and are able to use it for the benefit of patients.
Improving population health and preventing inequality
After extracting data from a variety of sources, including research databases and demographic data sources, health analytics software can help diagnose and report cohort models to evaluate the overall health status of a particular group of patients living in a given area. Ethnic group, exercising a certain lifestyle among other factors. Using predictive analysis, it is possible to predict the effects of lifestyle or living conditions on group health, set epidemic alerts, improve educational campaigns, and take other measures to improve population health.
In addition, a whole set of challenges for vulnerable patient groups can be addressed. These challenges include transportation, living needs and adequate income. Designing outreach strategies to address these challenges will make a difference for any healthcare provider. For example, by applying data analytics to patients ’SDOH, providers can search for low-income patients and advise them on where they can buy generic alternatives to expensive drugs.
Monitor the performance of the organization and increase concern
Custom-built medical analytics software allows you to monitor operations, employee efficiency, facility performance and more in real time. This helps to find a correlation between established workflows and patient health outcomes. As a result, organizations can redesign some of their processes, reallocate financing, resources and staff to be more efficient, and update health services to suit their current needs in a given area. These measures contribute to alleviating care delays:
- Overcoming staff shortages By specifying automated routines, tasks that can be performed in a more efficient manner and reallocate workforce across an organization.
- Taking stress on ER and hospital staff By enabling early diagnosis and warnings about possible complications to prevent critical deterioration of conditions requiring access to the ER. The same is true of hospital admissions in general – the number of inpatients with good preventive care can be significantly reduced, which reduces waiting time for those in need of hospitalization and improves hospital care.
- Acquiring more finance By making operations more transparent and results more measurable. Investors want to put their money towards clear objectives, where one can understand the reasons for success or failure. Analytics make it easy to understand the financial and operational flows of healthcare organizations.
The real-life case: Reducing hospitalization time with Smart Analytics
In the midst of the epidemic, a hospital in Pueblo, Colorado, had to join efforts with another local care facility. However, when the partners closed many of their units, the hospital had to onboard large numbers of patients. However, most inpatients had longer periods, which prevented new patients from being hospitalized.
To address the challenge, the hospital leveraged a new AI-powered device that looks at unstructured data and identifies factors that hamper patient discharge. The system then created a Discharge Checklist for the physician, which covered the disruptive factors for each patient and helped the physician resolve them.
The new equipment allowed for a 88% reduction in hospital stay. In the meantime, it has also helped them to achieve several positive changes: inpatient care was quickly addressed, patient satisfaction and loyalty increased because they received more personalized discharge pre-care, hospitalization could occur faster, and more patients could be enrolled. .
Although the epidemic is still far from certain, some efforts can be put in place to address key system loopholes and their disastrous consequences. In this case ‘disaster recovery’ should include a comprehensive approach to suggest effective measures at the government and local level.
The latter relies specifically on providers and whether they take action to cope with the health disparities their patients face. Collecting SDOH, adding this data to patient profiles, enabling health data analysis and taking data-based measures have proven to be significantly helpful. Through a joint effort, government agencies, health professionals and data analysis solution providers can help improve patient outcomes and population health locally and nationwide.
Photo: Goyer, Getty Images