The Trio Shaping the Future of Orthopedic Product Development

In the orthopedic industry, ensuring patient safety, minimizing infection risks, and optimizing cost-effectiveness are paramount considerations. To achieve these goals, three key elements play a crucial role: sterilization, reusable instrumentation, and packaging. In this article, we explore the best practices of this triad from an expert's perspective, emphasizing the impact on patient outcomes, operational efficiency, and environmental sustainability within orthopedic product development. 

Sterilization: A Critical Imperative in Orthopedic Product Development 

Sterilization is a fundamental aspect of medical device product development, manufacturing, and usage. By eliminating microorganisms and reducing the risk of surgical site infections (SSIs), proper design and sterilization protocols safeguard patient safety. The chosen sterilization method, such as steam, ethylene oxide, or gamma irradiation, will impact both material choice and part geometry.  

Many plastics are not temperature stable with steam and may degrade with gamma irradiation. Tight interfaces and blind holes challenge EO gas / steam penetration to all host sites.  And depending on how packaging is configured on a sterilization pallet, large devices may shield others from receiving a full dose of gamma ray.  The rigorous validation of both cleaning and sterilization processes adhering to regulatory standards are essential to maintain the orthopedic instrumentation and implants. 

Packaging: Safeguarding Integrity and Sustainability 

Whether it’s a single use peel pouch or reusable surgical case, orthopedic devices require specialized packaging to ensure product integrity, sterility maintenance, and efficient handling.  

Peel Pouch/Tray: Engineering seal width both for sterile integrity and ease of use.  Heavier devices are more likely to put stress on sterile seal unless properly constrained.  Right-size pouches within cartons to minimize creases, opt for a gentle roll instead. Execute verification tests after accelerated, real-time aging, and ASTM D4169 transit simulation. Testing doesn’t end at submission, incorporate in-process testing per ASTM F88 for ongoing vigilance.  

Reusable Surgical Case:  Layout instrumentation/implants as it makes sense for the procedure work-flow with spacing that allows steam ingress. Orientation angle should facilitate shedding of moisture, avoid any upward facing cavities as to prevent condensation pooling. A large amount plastic instruments in the tray puts you at risk of failing dry time testing.  Keep total tray weight within bounds of regional requirements.  

In addition, incorporating usability testing on packaging designs will help facilitate proper aseptic presentation and a smooth transition from sterile to non-sterile environments are essential for healthcare professionals. Optimal packaging design also considers sustainability aspects, such as the use of eco-friendly materials and minimizing excess packaging waste. 

Regulatory Compliance, Quality Assurance, and Continuous Improvement 

Sterilization, reusable instrumentation, and packaging are tightly regulated areas within the orthopedic industry. Regulatory bodies, such as the FDA and international standards organizations, provide guidelines and requirements to ensure the safety and efficacy of devices. Compliance with these regulations is essential to gain market approval and maintain patient trust. Manufacturers must establish robust quality management systems, conduct thorough validation studies, and implement effective quality control measures such as inspection and quarterly audits to ensure the reliability and consistency of sterilization, reusable instrumentation, and packaging processes. See ISO 11737, 11137, and AMI ST79. 

Thanks to orthopedic research, device designs are continually advancing highlighting new materials and features. This progress extends to quality improvement projects, sterilization methods, and packaging innovations. Collaboration between industry expert consultants and regulatory authorities is vital for driving innovation and ensuring that decision making considers whether changes can be adopted into previous studies or if new testing is required.  

Sterilization, reusable instrumentation, and packaging form a critical triad in the medical device industry, encompassing patient safety, operational efficiency, and environmental sustainability. Through meticulous sterilization protocols, the use of reusable instruments, and the development of optimized packaging solutions, orthopedic professionals can enhance patient outcomes, reduce costs, and minimize their ecological footprint. By prioritizing regulatory compliance, fostering continuous improvement, and embracing innovative technologies, the orthopedic community can maintain the highest standards of quality and safety. It is only through a comprehensive understanding and integration of sterilization, reusable instrumentation, and packaging practices that orthopedic product development teams can continue to evolve and flourish while delivering exceptional care to patients. 

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Authors

  • Eric Kennedy

    Principal Engineer | [email protected]

    Eric Kennedy is an engineer at Kaleidoscope Innovation based in Cincinnati, Ohio, and has over 15 years of global medical device experience leading large- and medium-scale concept-to-launch orthopedic, micro-surgical, cardiovascular and ophthalmic devices.

  • Matt Suits

    Head of Sales | [email protected]

    Matt has always loved interacting with clients to find solutions for their challenges. He was drawn to business development at Kaleidoscope Innovation because of the great potential he saw. After graduating from the Lindner College of Business at the University of Cincinnati, he worked with two startups, a marketing consultancy, a financial services company and the non-profit 3CDC. He believes that listening is the most important part of sales. In his free time, Matt enjoys movies, trying new foods, traveling and the great outdoors.

The Future of AI-powered Healthcare

What is artificial intelligence (AI)? Is it the evoking computer from sci-fi aware of its own existence and determined to destroy humanity? Is it a robot that does our job for us while we kick our feet up? Right now, maybe it is neither, it can be defined as “A System that mimics human intelligence to perform complex tasks using advanced learning algorithms that capture underlying patterns and relationships from the data they collect.”  The tasks and benefits from such a system can be many but generally serve as three main use categories: accuracy improvement, automation of tasks, or a recommendations engine.

In developing a SAMD (Software As a Medical Device) product consider both the regulatory guidelines and best practices.  The FDA is partnering with industry to develop regulations in this emerging field, they recently released a guidance on Clinical Decision Support Software describing the criterion in which software is considered a medical device by the agency. And, during software life-cycle development, ISO 62304 outlines the processes of risk management, maintenance, configuration management, and problem resolution.

Developers should build in systems on the front end for data mining whether in the form of document capturing tools, video data collection, speech recognition, or otherwise.  And, comprehensive cybersecurity around these data sources in addition to the access, analysis, and output systems.

Lastly, algorithms should take bias into account.  This is already present in the diagnosis making process today, clinicians can jump to conclusions based on early information and stick to their guns even as new information becomes available (premature closure / anchoring). The algorithms themselves can have bias, in how data is fitted when machine learning is automated.

  • Automation Bias: Tendency of people to show deference to automated output, maybe due to person’s lack of confidence/experience, or assumption that the automation designed to make the correct determination.
  • Fitting Bias: Over Fitting- Automation has been overly relying on the trained data and does not provide correct responses when given new information.  Under Fitting - Machine is under trained and doesn’t correctly identify relationships between the variables.

Widespread AI use is in its infancy, its currently being leveraged across several surgical products currently on the market including surgery planners, guidance systems, AR, blood loss monitoring, and predictive analytics. The future holds many opportunities for AI to burn down existing healthcare challenges.

Accuracy Improvement:

  • Comprehensive Patient Medical Information
  • Summarization and Highlighting of Patient Case History
  • Accurate Encoding of procedures and diagnosis for insurance
  • Accurate diagnosis from medical images
  • Risk-aware decision making –using predictive analysis of surgical outcome, implant choice, length of hospital stay, risk of re-hospitalization
  • Post op x-ray, feedback loop, feedback to surgeon on trending accuracy stats, predictive risks
  • Physician burnout - make less errors during diagnosis
  • Physician shortage – making fewer surgeons more efficient

Automation Enabled Improvements:

  • Improved surgical planning / operation
  • AI-assisted surgical robotics
  • Supply chain automation
  • Reduced non-conformances, out-of-commission instrument sets
  • Reduced waste, reprocessing costs
  • Smart intra-op assistant / training

Recommendations Engine:

  • Patient/procedure/surgeon customized device on demand
  • Fair surgeon success ratings based on predictive risk/outcomes
  • Informing consumers on surgeon/facility for their condition to maximize outcomes

It probably won’t be too far into the future before some of these AI-enabled improvements become mainstream practice in the healthcare domain. The recent advances in ChatGPT have shown how complex knowledge intensive tasks such as text summarization, essay generation, intelligent Q&A (Question and Answer), etc. can be accomplished by current language models.  Convolutional Neural Networks (CNN)-based deep learning models are showing promise for automatic detection and classification of tumors in medical imaging. Advanced Machine Learning (ML), Rule-based modeling, and Embedded-AI can help with addressing other opportunities such as risk prediction, improved surgical planning, AI-assisted robotic devices, supply chain automation, and customized recommendations

AI will help in bringing consistency in the process, improve overall efficiency, reduce cost of operations while adhering and improving the regulatory compliance.

Interested in implementing AI/ML technology into your business?

Kaleidoscope uses advanced learning algorithms to capture patterns and relationships within your data to help you better understand the data collected and provide both exploratory and predictive analytics based on findings. Contact Matt Suits: [email protected]

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Authors

  • Eric Kennedy

    Principal Engineer | [email protected]

    Eric Kennedy is an engineer at Kaleidoscope Innovation based in Cincinnati, Ohio, and has over 15 years of global medical device experience leading large- and medium-scale concept-to-launch orthopedic, micro-surgical, cardiovascular and ophthalmic devices.

  • Dr. Ravi Nandigam

    Principal Consultant

    Dr. Ravi Nandigam is a Principal Consultant in the Advanced Engineering Group at Infosys. He has 15 years of experience applying Artificial Intelligence, Machine Learning, and Software-based solutions in diverse Engineering domains. Dr.Nandigam is an inventor of a patent and author of many technical articles in peer-reviewed international journals on topics of AI/ML-based applications in Engineering.

  • Dr. Ravi Kumar G. V. V.

    Vice President and Head Advanced Engineering Group (AEG)

    Dr. Ravi Kumar is Vice President and Head Advanced Engineering Group (AEG) of Engineering Services, Infosys. He led numerous innovations and applied research projects for more than 26 years. His areas of expertise include mechanical structures and