Photo by Tima Miroshnichenko From Pexel
Clinical decision-making is one of the rare activities that require pinpoint accuracy. From population surveillance and health databases to diagnostics and clinical judgments, as well as drug procurement and financial accounting, healthcare organizations benefit from technological adoption on many fronts. In addition to facilitating treatment and managing data, technologies also aid in streamlining healthcare operations globally. More crucially, they make it possible to make sense of the ever-increasing volume of medical data.
The information is already being kept in digital form: More than 95 percent of hospitals and clinics have adopted EHRs. Sixty percent of hospital institutes in the United States currently use IoT devices, so they provide a sizable chunk of the data. The next major step for the healthcare business is the application of data analytics to multiple procedures related to patient care, servicing of equipment, and diagnostics following the receipt of information about patients or operations.
A fundamental responsibility of healthcare analytics is collecting and interpreting data to predict specific health situations. Please read our business intelligence article to familiarize yourself with the data processing jargon. Automation of medical data processing is becoming increasingly commonplace because of the use of technologies like machine learning. Therefore, we will investigate various healthcare IT cases that made a difference in recent years.
1. Improved Staff Allocation
To illustrate the potential of big data in healthcare, we begin by looking at an issue faced by every shift manager: deciding how many employees to schedule for a given shift. Adding too many employees can drive up optional labor expenditures. Insufficient staffing can lead to subpar patient care, which can have dire consequences.
Some Parisian hospitals are using big data to address this issue. In a new white paper, Intel describes how four Assistance Publique-Hôpitaux de Paris hospitals have been forecasting patient volume daily and even hourly using data from various sources.
Data scientists used “time series analysis” methods to sift through hospital admissions records from the past decade, one of the most critical data sets. The analysis revealed essential trends in hospitalization rates. Then, they might utilize machine learning to determine which admissions prediction algorithms were the most reliable.
The result of this effort is a web-based user interface that the data science team created to predict patient loads and aid in planning resource allocation via online data visualization, aiming to better patient care.
2. Improved CT Imaging and Patient Positioning
Growing patient loads, maintaining high image quality, and maximizing efficiency remain challenges for radiology departments. Due to Computed Tomography’s (CT) prevalence, radiology departments stand to gain significantly from AI-enabled technologies that improve CT operations and picture quality.
The first step is to put the patient appropriately for the examination. Mispositioning the patient is a common difficulty in CT, which can increase the patient’s radiation exposure or introduce noise into the image. Faster, more precise, and more consistent patient positioning is now possible thanks to AI-enhanced camera technology that can recognize anatomical landmarks in a patient. Radiation dose reduction and enhanced CT picture quality are two further benefits of AI-enabled image reconstruction that help to bolster diagnostic certainty.
3. Operation Duration and Success Rate Probability By Machine Learning
When it comes to pediatric and obstetric care, one case is worth mentioning. LPCH collaborated with HP Autonomy to create an analytical platform built on the HP IDOL search and analytics engine. IDOL is an algorithm developed to find meaningful relationships between enormous amounts of unstructured data. Using IDOL to standardize the information included within diverse healthcare data sources, LPCH provided doctors access to patient records in different clinical databases.
The system’s primary function is to link to different medical and financial information sources through its assistance of more than 150 data types and 400 data connectors. Data on previous therapy efficacy, symptomatology, and patient reaction to specific medications are made available to medical staff and management via visualizations and reports via a specialized analytical interface.
The SNOMED (Systematized Nomenclature of Medicine) taxonomy database, which contains over two million clinical concepts, is a part of LPCH analytics’ healthcare-specific capabilities. According to ICD, coding is also given for straightforward incorporation with existing healthcare infrastructure.
The ability to work with unstructured data was the main innovation introduced by the HP IDOL engine to healthcare analytics at LPCH. The industry’s standard analytical tools are limited to processing structured data.
4. Alerts and Warnings
One essential feature shared by additional analytics instances for healthcare data is instantaneous alerts. Clinical Decision Support (CDS) software is used in hospitals to analyze medical data in real-time and offer guidance to doctors as they make diagnostic and therapeutic decisions.
Despite this, doctors would prefer that people stay away from hospitals if possible. This is expected to be a top business intelligence trend in 2021 and may form the basis of a new approach. Patients’ health information will be continuously collected via wearables and uploaded to the cloud.
Additionally, doctors can evaluate this data in a socioeconomic context and adjust their delivery tactics due to access to the public health database. Organizations and caregivers will utilize cutting-edge monitoring systems to monitor this flood of data and respond immediately to any alarming findings.
If a patient’s blood pressure suddenly spikes, for instance, a doctor will receive a real-time alert and be able to take immediate action to bring the reading down.
Asthmapolis is another company that has begun using GPS-enabled trackers in inhalers to analyze asthma patterns in individuals and entire communities. Together, this information and that from the CDC are being used to improve asthma patients’ care.
5. Warning Health Signs in General Ward Patients
Similar applications of AI in healthcare to improve the human experience can be seen in acute and post-acute settings. Almost 20% of patients in medical-surgical wards will have a significant adverse event after surgery. Manually checking vitals is time-consuming and prone to mistakes.
Nurses and care teams can use AI-enabled systems to better detect early warning indications of events like respiratory failure and cardiac arrest by automatically tracking and monitoring vital signs and computing early warning scores. One hospital saw a 35% reduction in significant adverse events in the general ward and an 86% reduction in cardiac arrests.