Patient Flow and Bed Management Solution
The performance of many hospital departments depends on the way beds are managed across wards. Our client has developed a web app that allows a department to easily handle, manage and reorganize bed requests. The system increases usage efficiency of beds and minimizes any potential issues between patients who share the same hospital room.
In order to deliver better care and empower visually-impaired staff with automated hospital bed allocation, our client decided to overhaul and rewrite the app’s entire front-end.
As the existing front was outdated and not user-friendly, our mission included:
- Creating a brand new UX/UI design;
- Updating user-effectiveness & job satisfaction;
- Adjusting the operational efficiency;
- Enhancing the patient journey;
- Enhancing the accessibility design (contrast mode, font size, light & dark color scheme).
In order to gain a competitive edge regarding bed management software, our team completed:
- An investigation of national regulations about bed assignment;
- Detailed business analysis;
- New responsive design with an intuitive interface;
- Mock-ups with user-flow & multiple screens;
- Customizable user dashboards;
- Calendar for scheduling requests;
- Advanced search engine for filtering & sharing personal lists.
The software covers all necessary information about beds: whether they are dirty or clean; which rooms they are placed in; and patient-related information, such as address, relatives, insurance, etc.
The new intuitive interface guides the user all the way from the patient’s admission to a hospital, until final discharge. These changes boosted staff efficiency by saving time and allowing management to meet hospital performance targets.
With a graphical representation of wards and a list of the beds, it becomes easy for the bed management staff to carry out the allocation process. Information is updated in real-time, allowing a team to omit the manual counting process and improve turnover rates. Disabled users also feel more comfortable with the accessibility design when enhancing patient coordination, contributing to the hospital’s sound operability.
App for cognitive testing and training
MCI (mild cognitive impairment) and signs of early dementia are hard to detect; however, studies show that playing the piano has the ability to highlight even the slightest cognitive abnormalities.
Playing the piano involves multiple sensory processing, comprehension, quasi-simultaneous decision-making, and action execution. In short, it is a sure-fire way to analyze and evaluate brain functions and train minds. In order to test and prevent cognitive disorders, our client (a musician) came up with the idea of building a piano emulator from scratch.
In order to build a piano emulator app that could detect and calculate the probability of developing cognitive abnormalities associated with MCI and early-stage dementia, the emulator had to be:
- Fast and accurate, with gamified cognitive checkups that fit the clinical workflow;
- Suitable for assessing pre-clinical, early dementia and MCI;
- Operable by a non-specialist.
The app was released with the following features:
- Visuospatial and motor encoding;
- Time-stamped finger responses;
- Audio cues;
- Integrated the tool for different PACS archives/VNAs compatibility;
- An algorithm that collects user-response and analyzes data;
- Integrations with EEG and eye-tracking;
- Recording history;
- Data export;
- GDPR and HIPAA compliance.
This app evaluates executive functions, such as inhibition control, attention, multisensory memories, and various brain-region activities. In this way, the software acts as a dementia prevention tool, contributing to remote care for cognitive monitoring. As for now, our client is looking for opportunities to collaborate with Philips as their headsets could enhance app performance and function.
This software is expected to launch as a medical device in the EU at any moment.
Radiology Workflow Management Tool
The product allows hospitals to automate the workload and distribution of studies for interpreting radiologists. To meet the SLA time frames, the app’s AI engine defines exam priorities, locates qualified doctors (based on their preferences), and assigns a specific exam to a particular expert. The software balances the workload so that all radiologists receive the correct amount of work they are committed to cover.
The main difficulties in the previous version of the app, which became the source of customer disappointment, were poor operational performance combined with a confusing UI. Medium to large hospitals with a significant study turnover rate reported that slow app responsiveness and overall balancing inefficiency were affecting the number of medical exams reviewed. Following some fruitless attempts with other teams to redesign the software, the client finally turned to us to help improve the overall performance right away.
To improve impending faults and to preserve existing customers, we set out to:
- Find and fix the reasons linked to the app’s slow performance;
- If needed – change healthcare app architecture to raise performance;
- Verify the studies distribution algorithm and implement the desired business-logic elements;
- Significantly decrease the capacity of the infrastructure required to use the application;
- Improve app UI to make it more responsive and user-friendly.
After putting out the fire, we directed the next flow to complex tasks.
- To build an integration with the majority of existing dictation systems and image viewers;
- To gain a competitive edge and remain independent from existing market leaders (Philips, Agfa, etc.);
- To increase patient satisfaction and shorten the time of studies’ review and finalization.
After the execution of highly detailed performance tests, our developers reviewed all API calls and database requests and challenged every query’s adequacy.
As a result, we tightened data retrieval and increased the volume of acquired data every step of the way. A large portion of data was cached in order to speed up access to repeatedly queried but rarely changing medical data. Afterward, we implemented the composite pattern approach to enable complicated and nested filters to be compounded, allowing for the instant extraction of data.
- Crafted UI / UX for both doctors and hospital admin requirements;
- Completed integration with multiple image viewers/dictation systems;
- Added a flexible and robust load balancing mechanism for real-time distribution;
- Integrated the tool for different PACS archives/VNAs compatibility;
- Created a mechanism for scheduling systems interoperability;
- Created an internal audit mechanism to control data access (for HIPAA compliance);
- Developed a reporting tool for physician performance tracking.
After initial months of working around the clock, the AI engine’s performance and API back-end responsiveness has improved significantly. Presently, the initial infrastructure can efficiently support hospitals with up to 10 million studies per annum. Furthermore, the business-logic of studies distribution was challenged and fixed throughout. Together with UI updates, these achievements lowered the overall page responsiveness to less than one second. Our collaboration has lasted for over 2 years and continues successfully as of the time of writing of this case study, making it possible to polish the product with innovative and competitive features.