Questions dominate the healthcare world. What is the proper diagnosis for this patient? How can we staff our team more effectively so patients get seen faster? Are we doing everything we can to ensure the best possible outcome for this patient? These questions and more are at the forefront of workers in the healthcare industry, and if they are not well informed, the decisions could be about the difference between life and death.
In this blog, we’ll explain how data can be used to make informed decisions to healthcare-related questions like these, why phData should be your choice to take you through your data journey, and an example of how phData has helped a healthcare company like yours.
Healthcare Questions phData Can Answer with Data
How can we better predict when a patient will have a medical episode?
What if you could accurately know when a patient is going to have a heart attack or a stroke before it happens? How many lives of high-risk individuals could be saved with this knowledge? That’s exactly what predictive medical diagnosis aims to achieve using a patient’s medical data.Â
By collecting patient data, with their expressed consent, of course, through Electronic Medical Records (EMRs), wearables, and biosensors, a rich dataset can be created of the state of the patient’s health. EMRs include patient demographics, medications, lab results, diagnoses, and procedures, while wearables and biosensors track heart rate, sleep patterns, and activity levels. All these data points could be used in an analytical dashboard to estimate the likelihood that the patient will have a medical episode or be used to determine if they have a disease that may have gone undiagnosed.
Taking it one step further, we at phData can use the patient’s data to compare to historical medical data of patients with diseases to create machine learning models to find indicators of a medical episode that medical staff may have overlooked.Â
However, certain considerations and cautions are required when working with a patient’s medical data. Data security is paramount to keeping patients’ data private, and data quality needs to be perfect to create an effective analysis. Any machine learning models also require strict explainability, so doctors will trust the model’s recommendations.
How can we improve clinical diagnoses?
Diagnosing illnesses heavily relies upon physicians’ experience and intuition while examining X-rays, MRIs, and mammograms. However, human eyes are only well… human, and subtle abnormalities can be easily missed. This is where many health professionals have started to work with computer vision models as a second set of eyes.Â
Computer vision works by training learning models on images of diseases, which can then be used to help find abnormalities in images such as X-rays and MRIs. For example, a computer vision model can be trained to find cancerous regions of lung tissue, potentially saving the physician the laborious task of looking through hundreds of images. This could lead to an earlier detection of the cancer and save a life.
Computer vision sounds like a challenging area to start. Still, with phData and our computer vision partner, Landing AI, to help, this new technology can be set up easily, sometimes within hours. LandingLens, the computer vision software created by Landing AI, doesn’t require any prior machine learning knowledge and can be trained on as little as 10 images.Â
Here is a screenshot of an actual application in LandingLens that is used to detect tumors in lung tissue:
How can we limit the turnover rate?
Turnover is a major problem for the healthcare industry, with hospitals expected to turn over 100% of their workforce every five years. Hospital leaders often ask how we can retain our talent and if there is a way to predict which employees may want to leave. Again, the answer is all in the data.Â
Employee data can be a goldmine for predicting turnover, allowing you to intervene and retain your valuable staff. Data such as employee reviews, tenure, promotions, compensation, and exit interviews can all be collected to create a view of what an employee who is likely to leave looks like. With the help of phData’s team of experts, this data can be used to create statistical models to predict the likelihood that a current employee will want to leave within the next six months.
This data can help healthcare providers retain their key talent and save hundreds of thousands of dollars in yearly recruiting costs.Â
Why phData?
Many data engineering consulting companies could also answer these questions for you, or maybe you think your team has the talent to do it in-house. Why should you choose phData to help?Â
Expertise
Here at phData, we strive to be experts in data engineering, analytics, and machine learning. Our engineers have worked with many businesses like yours and can use that knowledge to answer questions with data you may have never thought possible. We continuously learn and grow as a team to adapt to the ever-changing data landscape and ensure we’re current on all the latest technologies.Â
Partners
We are partnered with dozens of companies that create data and machine learning engineering tools and have advanced knowledge of them. This allows us to recommend the best tooling for the job, which can make DE and MLE faster, more efficient, and cost-effective for you and your team.Â
Automation
With our extensive project experience, we have created in-house automation tools for many DE tasks, especially when using Snowflake AI Data Cloud as your cloud data provider.Â
These include (but are not limited to):
SQL dialect translation
Data migration validation
Synthetic data generation
Information Architecture provisioning
These automation tools are free for all phData customers in perpetuity.Â
Case Study
phData has helped many healthcare companies answer tough questions by utilizing their data. Here is just one example of what we’ve done for other organizations just like yours:
Problem
A thriving medical company needed further development for its algorithm to stimulate the airway during sleep apnea events. They wanted to determine how much they could improve their algorithm with machine learning & AI to create a better solution.
Solution
phData experimented with various machine learning methods to determine what approaches would be viable in improving the existing algorithm. To do this, phData builds an MLOps platform in AzureML to enable the training of various advanced modeling techniques, such as Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN), Fully Convolutional Networks (FCN), XGBoost, and TapNet.
Results
Upon completing phData’s engagement with the customer, phData set up a machine learning platform and built models that would allow the customer to see what improvement could happen if both approaches were combined.Â
Additionally, phData took a creative approach to the customer’s problem, which gave the customer options for furthering their development through best practices in machine learning/AI.Â
Closing
phData experts have the knowledge and experience to answer these healthcare questions and more using modern data engineering and machine learning techniques. Whether you’re trying to diagnose patients more effectively or simply need to know the best way to staff your office, you have all the answers already in your data.
Partner with phData and unlock the potential of your data to help your company and your patients.
