Using AI to Support Rehabilitation Planning in Action Apps
- Jo Clubb
- 22 minutes ago
- 7 min read
This post explores Action Apps’ new AI-generated rehabilitation planning feature.
Rehabilitation planning is a complex process. Practitioners need to combine the diagnosis, clinical findings, imaging, objective markers, scientific evidence and the individual athlete’s context, before translating all of that information into a clear and adaptable plan.
That process takes time. It also requires information to be organised in a way that can be understood not only by the rehabilitation team, but by coaches, performance staff, medical directors, other stakeholders across the organisation, and the injured athlete.
Action Apps has developed a new AI-assisted rehabilitation planning feature to support this process. In the accompanying video, I demonstrate how the tool can generate an evidence-informed rehabilitation plan using a grade 2 biceps femoris hamstring injury as an example.
The important point is that the AI is not making rehabilitation decisions on behalf of the practitioner. Instead, it helps to structure the available information, apply the selected evidence and methodology, and create a detailed starting point that can then be reviewed, edited and individualised.
Building the Athlete and Injury Profile
The process begins within the Return to Play module, where the practitioner creates a new plan and enters the relevant injury information. In the example demonstration, I use a grade two strain of the biceps femoris as the diagnosis.
This information provides the context from which the AI generates the plan. As with any use of artificial intelligence, the principle of garbage in, garbage out applies. A vague diagnosis and minimal clinical information are unlikely to produce a sufficiently specific or useful rehabilitation plan. So incorporating the injury mechanism, symptom location, clinical findings and MRI results, as well as current functional limitations are all useful inputs.
As with any use of artificial intelligence, the principle of garbage in, garbage out applies
The more relevant context we can provide, the better the system can understand the problem we are trying to solve. That does not mean adding information for the sake of it, but it does mean clearly describing the injury, the athlete’s presentation and any factors that may influence rehabilitation.
Further context might include previous injury history (as we know that is the strongest influence of future injury risk), age, playing position, current stage of the season, or specific performance demands.
This context matters because the same diagnosis does not necessarily lead to the same rehabilitation plan.
Adding the Evidence Base and Methodology
One of my favourite features of this new tool from Action Apps is the ability of select the specific evidence and methodology that underpins the AI-generated rehab plan. This might include published research or resources that are included in the system already, as shown in the screenshot below.
And it's not just limited to external research, the team can upload their own resource. If for example, the club has developed its own internal return-to-running framework, hamstring rehabilitation model or return-to-play testing battery, this proprietary information can also be uploaded, safely and securely, within their own solution and help to inform the rehab plan.
The resources selected to inform the plan are then cited in the generated documents. This provides greater transparency around how the AI has generated its recommendations and allows the practitioner to review the original sources.
Referencing the evidence does not remove the need for critical appraisal, but it makes the process more transparent and gives the practitioner a clearer opportunity to judge whether the selected evidence is appropriate for the athlete and context.
This also means the plan in not limited to relying on a generic rehabilitation model. The published evidence can be combined with the team’s own frameworks, terminology and operating procedures.
Data Security and Privacy
The use of AI within sports medicine and sports science also raises important questions around athlete data, privacy and security. So I asked the team at Action Apps to clarify how they approach that with this tool, as well as the platform more generally. They said:
"Data security and privacy are fundamental principles of the platform's design. Built on the Microsoft cloud ecosystem, the solution leverages enterprise-grade security features including encryption of data both in transit and at rest, role-based access controls, multi-factor authentication, comprehensive audit logging, and continuous monitoring to protect sensitive information.
The platform is designed to support regulatory frameworks such as HIPAA by providing the technical capabilities required for the secure storage, processing, and management of protected health information (PHI). Combined with appropriate organisational policies, governance, and operational controls, this architecture enables organisations to maintain a secure, compliant, and resilient environment for managing confidential data."
Generating the Rehabilitation Plan
Once the relevant information, evidence and methodology have been entered, the platform generates the rehabilitation plan. As you can see in the video demonstration, it generates:
A risk summary
A prognostic summary
Pre-injury baseline data and RTP targets
RTP Swimlane with phases, intervention, and exit criteria
Monitoring and reinjury risk mitigation plan
All of these sections remain editable. The practitioner can change the language, adjust the thresholds, remove an inappropriate recommendation or add information that has not been captured by the generated output.
This is an important distinction. The plan is AI-generated, but it is not intended to be accepted uncritically or applied without modification.
Respecting the Non-Linear Nature of Rehabilitation
Breaking rehabilitation into phases helps us manage a complex process, but it can also give the impression that recovery is linear.
In reality, athletes do not always complete one phase perfectly before moving into the next. Different components of rehabilitation often overlap, and progress in one area may occur more quickly than in another.
This non-linear and adaptive nature of rehabilitation has recently been discussed by Matt Taberner, Tom Allen, Matt Jordan and Dan Cohen in their Physical Therapy in Sport article that is now published online. In the figure below, they used the metaphor of We’re Going on a Bear Hunt to illustrate the uncertainty and complexity involved across the return to sport continuum.
Their work also highlights the number of people and interacting factors within the rehabilitation ecosystem. The process involves far more than a single practitioner applying a pre-determined protocol.
The Action Apps feature does not remove that complexity. Instead, it provides a structure through which practitioners can try to manage it. We still need phases, assessments and objective markers, but we also need to continually zoom in on the athlete’s response and zoom out to consider the wider rehabilitation picture.
Communicating With the Wider Team
Given the wide network of stakeholders in the athlete ecosystem, rehabilitation plans also need to support communication.
The athlete’s status may need to be explained to coaches, performance staff, the medical director or, in some environments, senior leadership within the organisation. Each stakeholder may require a different level of detail, but the underlying information should remain consistent.
Action Apps allows the plan to be exported as a PDF, providing a clear overview of the athlete’s current phase, completed objectives, outstanding targets, projected timeline and supporting evidence.
Generated at a click of a button, this reduces the time spent manually compiling updates and helps to ensure that the wider team is working from the same information. Of course, a report does not replace the conversations that need to happen around the athlete. However, it can provide a useful shared reference point for those discussions.
AI as a Support Tool
The clearest value of this feature is the time it can save practitioners when compiling and structuring a rehabilitation plan. It can bring together the injury summary, selected evidence, return-to-play targets, phased interventions, monitoring assessments and reporting within a single workflow. This reduces some of the administrative burden associated with creating and updating these documents manually.
However, the practitioner remains central to the process.
They must decide what information to enter, which evidence and methodology to use, whether the generated recommendations are appropriate and how the plan should change in response to the athlete.
AI can support the organisation and communication of rehabilitation, but it cannot fully understand every interaction, symptom response or contextual factor affecting the athlete.
The aim is therefore not to hand over the rehabilitation process. It is to use AI to make parts of that process more efficient, while preserving the critical thinking and professional judgement that effective rehabilitation still requires.
Final Thoughts
Rehabilitation is complex, individual and rarely linear. To manage that complexity, practitioners need to break the process into components, set objective markers and monitor progress. At the same time, they need to remain flexible enough to respond when the athlete does not follow the expected path.
Action Apps’ AI-generated rehabilitation planning feature provides a structured way to bring that information together. Within minutes, it can create an evidence-informed plan containing phased interventions, return-to-play targets, exit criteria, monitoring assessments and progress visualisation.
The output is fully customisable and the evidence is referenced, but it still needs to be critically reviewed by the practitioner.
Used in that way, AI can reduce the time spent compiling plans and reports, allowing practitioners to focus more of their attention on interpreting the information, adapting the process and supporting the athlete.
The aim is therefore not to hand over the rehabilitation process. It is to use AI to make parts of that process more efficient, while preserving the critical thinking and professional judgement that effective rehabilitation still requires.
📄 Explore more:
Learn more about Action Apps
Watch the full demonstration 'Building an AI-Generated Rehab Plan in Action Apps'
FAQs
Can AI generate a complete rehabilitation plan?
The Action Apps medical tool can generate a detailed starting point based on the diagnosis, evidence, methodology and athlete context provided. However, the practitioner should review and individualise every section before using it to guide rehabilitation.
Does the plan reference the scientific evidence?
Yes. The journal articles and methodological sources used to inform the plan are included as references. Practitioners can also add their own internal frameworks and processes.
Can the generated rehab plan be edited?
Yes. The practitioner can adjust the targets, phases, interventions, exit criteria, monitoring assessments and written summaries.
Does this replace clinical judgement?
No. The feature is designed to support planning, organisation and communication. The practitioner remains responsible for evaluating the output and making decisions about the athlete.
Can the rehabilitation plan be shared with other stakeholders?
The plan can be exported as a PDF, providing a clear overview for coaches, medical directors, performance staff and other relevant stakeholders.
This article is support by Action Apps. For more information about their services, visit their website.
Subscribe to the blog to stay updated!