Sleep plays a vital role in your good health and well-being. Getting enough sleep can help protect your mental health, physical health and quality of life. Sleep Recorder is an application for your iPhone that simplifies adding data points to Apple's Healthkit.
The CMS Data app displays hospital-specific ratings and charges for the more than 4,000 U.S. hospitals that receive Medicare payments.
Track tenant packages delivered to your building with Tenant Package Tracker. Notify your tenants when they arrive by email, text of phone. Record tenants signature when package is delivered.
Rental Property and Work Order Manager allows you to track and manage your rental property from your iPhone or iPad.
Simplify recording your daily body weight in HealthKit with Save The Weight an easy to use selector and one button save feature.
The Buh! app uses the speech synthesizer on your device to speak text associated with pictures you take or choose from your photo library.
If you are a consultant, contractor or gig worker this app will help you track your hours and expenses and simplify the customer invoice process.
Open Caption is a continuous speech to text app that uses both Apples and your devices speech recognizer to transcribe spoke word.
Bridge Plans help establish organizational goals, organize managers and directors to achieve individual and organizational targets, and redesign operations to encourage efficiency and change management. Click here to learn more about Bridge Planning.
The bridge planning app allows you to track the progress of your organizations operational and financial improvement projects on your mobile device (iPhone or iPad).
The hospital admission process is an extremely complex and interrelated process with many critical factors impacting it. Analyzing and documenting delays is a time consuming and manual task.
Using artificial intelligence machine learning to identify reasons for delays in the hospital patient admission process can help.
Participated in the operational data challenge posed by the Medical Analytics Group at MGH. The challenge consists of building machine-learning models to predict the wait time for four radiology departments at separate facilities.
Using a months worth of data extracted from a OR management system we built a machine-learning model to predict whether the scheduled duration of a surgical procedure is enough time or too much time.