Sample Healthcare Paper on Artificial Intelligence in Radiology and Cardiology

Initiation

The proposed project involves the use of artificial intelligence (AI) to perform repetitive tasks such as conducting test analysis. In particular, the AI systems will be used for analyzing tests such as X-rays, CT scans and for data entry in relation to those tasks (Jiang et al., 2017). The objective of the project is to reduce the percentage of time taken by radiologists and cardiologists to analyze tests and hence improve the number of patients that can be attended to. The radiologists and cardiologists would be left to only analyze complex results that cannot be interpreted without supervision. The project initiation phase would involve educating the stakeholders of project implementation about the benefits of AI in healthcare and in performing repetitive tasks. Project plans and budgets would then be developed and submitted for approval before progress into the actual implementation phase.

Project Scope Statement

Description of the project

The proposed project is an AI system to be used for repetitive mundane tasks that can be performed faster and accurately using robots. The amount of data in cardiology and radiology is immense and can be overwhelming to those involved. However, there are several tasks in those areas that can be done by robots under minimum supervision. The project will therefore aim at delegating these mundane and simple tasks related to results analysis to robots. The radiologists and cardiologists therefore have to be involved only in complex data analysis where strong supervision is required.

Purpose of the project

The purpose of the project is to reduce the workload and work hours for radiologists and cardiologists by delegating some of their mundane tasks to robots.

Project Objectives

The key project objectives here include:

  • To increase awareness about the role of robots in the performance of repetitive tasks.
  • To improve the work environment for radiologists and cardiologists by reducing their work load.
  • To improve the efficiency and quality of cardiology and radiology functions through more effective results analysis and optimum time allocation for the radiologists and cardiologists.

Project assumptions

While beginning to work on this project, some of the assumptions that will be put in place include:

  • The robots to be used will be programmed accurately to foster accurate results interpretation.
  • The workload at the healthcare facility in the two departments is sufficiently large and is hampering the effectiveness if cardiologist and radiologist performances.
  • There is delay in reporting from the radiology and cardiology departments and that delay can be attributed to the high workload carried by those in the two departments.

Project constraints

The major project constraints in the use of AI in the two departments include financial constraints and time limitations. The implementation of AI will require high capital investment, which may not be easily accessible (Perez, Deligianni, Ravi, & Yang, n.d). Similarly, the timelines for the implementation may be a constraint due to dependence on administrative approval prior to implementation.

User Requirements Matrix

Process Data Quality Measure User Requirement Must
1.      Analysis of test results ·         Consistency of outcomes.

·         Accuracy of results.

Yes
2.      Invasive diagnosis Results accuracy. Yes
3.      Complex diagnosis ·         Accuracy of the results.

·         Consistency with professional interpretation.

No
4.      Data recording Accuracy of records. Yes

Project Deliverables

The most important project deliverable for the proposed project will be an AI system integrated into the general electronic health recording system that will be used for analysis and recording of radiology and cardiology test results. Other deliverables will include complete awareness of the workings of the integrated AI system by radiologists and cardiologists at the hospital, and a robust monitoring and maintenance process for the AI system to avoid chances of misdiagnosis or program errors.

Project Milestones

The table below provides a summary of the project milestones as intended in the proposed project.

Key stages Key Milestones Start Date End Date
Initialization ·         Creating awareness of the need for the project.

·         Planning for the project implementation phase.

·         Budget creation and approval.

·         Seeking proposals for the complete AI system and integration with electronic health recording.

25 April, 2019 20 May, 2019
System development ·         Designing of the AI systems in use and submission of designs for approval.

·         Training users on the nature of the design.

·         System development.

·         Training users on the use of the system.

21 May, 2019 31 December, 2019
Project implementation ·         System go-live

·         Initialization of system use.

·         Monitoring the use of the system and its performance.

·         Continued system maintenance and supervision.

 

1 January, 2010 Continuous

Planning

Project Description

The actual project to be implemented includes training the stakeholders about the project, designing and development of the actual AI system, and implementation of the system. The project will conclude with guidelines on continuous maintenance and monitoring of the system performance.

Project Outcomes

The three project outcomes for the proposed integration of AI into radiology and cardiology results analyses include:

  • Awareness of the project rationale and use by the intended stakeholders.
  • A system that will be able to analyze, and interpret results of simple radiology and cardiology procedures.
  • An effective workforce that can work with the developed AI system to achieve the goals of providing quality and safe healthcare to all patients.

Purpose of the project management plan

The purpose of the project management plan is to provide a guideline to the project team to be able to focus on the key project deliverables and timelines.

Key Project Deliverables

Key Deliverable Deadline
Submission of project plans and budgets. 30 April 2019.
Receiving and evaluation of project proposals. 20 May, 2019.
Project design approval. 30 June 2019.
System development – to deliver high quality results analysis and diagnoses. 31 November 2019.
Training of users. 25th December 2019.
Project go-live. 1st January 2020.
   

Execution

During the project implementation phase, various activities will be on-going. The most essential activity in any project is the communication process. Information needs to be shared among the project team members, supporting stakeholders and the management. Future users of the system also have to be informed of progress in its development so that they can give their inputs frequently. For this project, communication will be achieved through various methods such as telephone calls, brochures, text messages, memos and e-mails. Additionally, there will be scheduled meetings for the team members on a bi-weekly basis. These meetings will be the platforms for reporting on progress and planning for the next phase of action.

Participating members will include: the project manager, the hospital’s administrative manager, the head of radiology department, head of cardiology department, and selected IT team members. The project manager will be responsible for coordinating all project activities, inviting members to meetings and following up on specific responsibility performance while the administrative manager will represent the interests of the organization. The heads of radiology and cardiology departments will be responsible for coordinating with their respective teams to determine the needs and gaps to be filled by the AI systems, to collaborate in providing information to the project team and to give feedback to their departments. The IT team members will be responsible for actual system design, development and user training.

Project Monitoring and Control

Each of the team members will have specific tasks assigned to them with different performance measurement metrics and key results. During the bi-weekly meetings, all members will be expected to report on their progress and to provide detailed expectations for the next two weeks. The reports should be written and presented in both hard copy and PowerPoint formats. Status updates will be shared by the project manager to the administrative manager who will share them with the hospital teams to ensure alignment with the project progress. Progress reports will be developed at the end of each of the project stages by the project manager in collaboration with the administrative manager and shared to all the project team members. Any project changes will be communicated officially by the project manager during the bi-weekly meetings.

Project Closure

Working on this project has the potential of enhancing the understanding of the project team on the role of AI in healthcare. It also reveals more about how much AI can contribute to better healthcare. From this project, there have been various lessons learnt. For instance, it is now clear that achieving the objectives of such a complex project requires multidisciplinary collaboration. This is aligned to the discussion presented by de Wolf (2017), who alleges that every project requires a multidisciplinary approach to decision making due to the need for interactive information sharing. Another lesson learnt is that project management needs to be an active rather than a passive activity, in which continuous and iterative decision making is required. Once the project go live has been completed, all the project resources will be accounted for. Any resources left behind after project completion will de-allocated to future projects.

References

De Wolf, R. (2017, April 10). What project management system does the multidisciplinary team want? A requirement study to compare the users’ wishes with two project management systems. Master’s Thesis – University of Twente. Retrieved from essay.utwente.nl/72231/1/Wolf_MA_BMS.pdf

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., et al. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230. Retrieved from svn.bmj.com/content/2/4/230.info

Perez, J.A., Deligianni, F., Ravi, D., & Yang, G.Z. (n.d). Artificial intelligence and robotics. Retrieved from arxiv.org/ftp/arxiv/papers/1803/1803.10813.pdf