Decision-making has one clear goal in medicine — the good of the patient. The digitalization of the healthcare environment has made the good of the patient increasingly dependent on the intelligent use of data.

The challenge is integrating a wide variety of data from clinical, radiological, laboratory, pathological and observational sources in a way that promotes the highest possible quality standard for decision-making. This must be done while consistently centering the patient’s needs and wishes.

Yet, complex decision-making processes often fail because patient data is inaccessible, too extensive or unstructured. Often, these complexities create flawed guideline adherence. A digital platform that prepares a wide variety of data from diverse systems and institutions in a user-friendly, simple and flexible way could assist in resolving decision-making bottlenecks and help to prevent errors.

Challenges arise along the patient pathway

The quantity, availability and compatibility of data and data sources in healthcare creates an unmanageable workload for medical professionals. Physicians have little time to sift through thousands of individual data points in electronic medical records to extract the information relevant to the patient’s case. This means that a great deal of electronically stored data is never used.

Medical data is useless, however, unless it can be transformed into actionable insights. That transformation requires analytics expertise. Without this expertise, the volume of data in healthcare can lead to a type of “information overload” that can make frontline healthcare professionals skeptical of the idea of digitalization.

For this reason, advanced digital solutions that automatically analyze patient data and present it in a user-friendly, clinically meaningful way are urgently needed. These advanced solutions will help prevent the issue of “filter failure” by providing a ready-made suitable selection and processing mechanism.

Aside from these challenges, a further concern is the failure to comply with clinical guidelines in therapeutic decision-making. This can lead to an increase in complications risk, costs and lengths of stay.

A platform-based approach improves outcomes and workflow

These challenges will be hard to solve without the right digital support for clinical decision-making. A complete digital solution must provide support for clinical decisions that includes clinical guidelines, patient data summaries, condition-specific order sets, diagnostic support and contextually relevant reference information, as well as providing automated alerts to avoid errors. Machine-learning algorithms and AI-based approaches will help avoid unnecessary procedures and facilitate good decision-making along every step of the patient pathway, including surgical decisions. Specialties like oncological care will benefit from digital support that will help to increase adherence to guidelines, reduce treatment costs and ease physician workload.

A comprehensive solution, which supports diagnostic and therapeutic decision-making for optimized patient outcomes, is the teamplay digital health platform. This solution can help to improve operational decision-making for efficient workflows. The teamplay digital health platform allows the integration of existing IT systems within an organization and across shared institutional boundaries, such as other hospitals, practices and pharmacies.

Such a digital health platform is already in use at MedStar Health, a large health network in Washington, DC, and Maryland. Using a workflow orchestration application, the platform has improved the coordination of image reading, allowing all MedStar radiologists to work together as one team regardless of location.

Another example that shows the value of this digital health platform and its applications is Zwanger-Pesiri Radiology. Deploying a functionality of the platform enabled this large multi-site radiology practice to increase MRI throughput from two patients per hour to three. They also saved valuable time in editing and distributing scanning protocols by using teamplay Protocols. This practice further employed AI-Rad Companion, a cloud-based interpretation tool, which can deliver additional valuable information that is often missed in the initial reading.

Another application offers automatic processing and structured display of patient data from multiple sources, including written texts. This makes it faster and easier for a multidisciplinary team to prepare and discuss patient cases to make decisions about treatment plans for individual patients. This application also integrates clinical guidelines, individual risk stratification, and patient preferences, allowing for evidence-based and transparent recommendations regarding treatment options.

Flexibility is key during the digital transformation

Innovations in IT continue apace, as does digitalization of the healthcare environment. Innovative medical technology companies can supply healthcare providers with digital infrastructure that is simple, versatile and adaptable.

To fill this need, a growing number of intelligent applications are required. These applications must deliver meaningfully prepared networked data for operational and clinical questions. Furthermore, the current pace of digitalization is also changing the nature of medical decision-making. Therefore, all newly developed solutions need to grow at the pace of this new ecosystem of data and data management solutions.

The continual advance toward an optimized smart data healthcare environment is driven by integrative, interoperable system- and vendor-neutral solutions, such as the teamplay digital health platform. This kind of digital suite of solutions can minimize the isolating effects of data silos and promote holistic decision-making that benefits patients and improves the efficiency of healthcare services.

To learn more about holistic decision-making in a digital world, read the full whitepaper here.