Implementation of 3D Printing in Various Healthcare Settings: A Scoping Review.

3D printing has been one of the recent buzzwords, along with Machine learning and AI. The combination of these three provides a great deal of improvisation in health education and healthcare management techniques. This paper studies various implementations of 3D printing solutions. Shortly, AI combined with 3D printing would revolutionize the healthcare industry in most areas, not just limited to human implants, pharmaceuticals, tissue engineering/regenerative medicine, education, and other evidence-based decision support systems. 3D printing is a manufacturing method in which objects are made by fusion or depositing materials such as plastic, metals, ceramics, powder, liquids, or even living cells in layers to produce a desired 3D-Object.

The Journey of Clinical Registries Through Various Phases of the Digital Age: A Technical Perspective.

The concept of registry-based medical research goes back to the mid of 18th century where data was collected in actual physical registers and analyzed using manual counts in a very primitive way until computing technologies took over to digitize information, to change the process all the way from data collection to data analysis. This digital age of technology can be hypothetically classified in 3 eras; the Digitization Era, the Integration Era, and finally the Futuristic, Smart Intelligence Era. This study would highlight the changes in the fundamental aspects of a medical registry under each of these digital eras.

Urgent Need for Developing a Framework for the Governance of AI in Healthcare.

Lately, the application or integration of Artificial Intelligence in various areas of the Healthcare domain has been a prime attraction; this includes diagnostics, medicine/drugs, medical devices, interventions/procedures, imaging, therapies as well as treatment regimes, and these areas are in direct relation with the patient care, which is the core subject of the improvements envisioned through the implementation of AI. Although carrying this practice with a focused objective of improvisation in providing quality care, the overall concept of such implementations misses the governance path which can comply with any available regulatory environment, which unfortunately at this stage does not exist. As these implementations would have a direct impact on patients care, there is an urgent need to institute a robust governance and compliance framework in order to ensure the efficacy, safety, privacy, and ethical considerations. The onus of pioneering this initiative of building a governance framework for the implementation of healthcare artificial intelligence primarily rests with the Food and Drug Authority of the respective country, it is also important for this authority to further organizing the governance framework in agreement or collaboration with other international authorities.

Developing a Framework for a Healthcare Data Science Hub; Challenges and Lessons Learned.

'Research through innovation' is the current demand echoing throughout the healthcare industry, healthcare institutions tend to invest heavily in technology. Data Science being the major disruptor across industries is being incepted through establishment of innovation and R&D centers within their respective organizations. Data Science has become a critical component for the healthcare industry, supporting innovative approaches towards advanced clinical practice, clinical research and corporate management, serving to build an intelligent enterprise. Every healthcare institution maintains a good number of technical staffs with IT, Software, data management, BI and analytical capabilities, aiding the institutions to manage report and publish its data in some or the other way, grossly covering most aspects of data science knowingly or unknowingly. Setting up a new entity within the organization by recruitment of staff with Data Science based skill sets would be the first thought to strike the management, which in contrast would end up as disaster when it comes to understanding the organizational culture, processes, infrastructure, platforms, data etc. Hence in order to setup a data science hub, regrouping or realigning some of the existing institutional resources is crucial. With this approach, the Data Science hub would carry out three primary functions. The "Project Management & Data Sourcing", the "Data Management & General Analytics" and "Advanced Analytics". Current resources can be reorganized within the first two functions, further; it would be about establishing an advanced analytics group within the hub which would perform the Machine learning and AI functions.

Revisiting the Skills of a Healthcare Data Scientist as a Field Expert.

The buzz words 'Data Science' and 'Data Scientist' are trending high in this age of information. The boundaries are still undefined, the exact skill sets are unclear, and the job description is still murky. This is an attempt to identify some mandatory or desired skills based on what data science demands from a data scientist. A very generic job description for a data scientist is 'A person who can perform advanced analytics on the institutional data', this gives a very unclear picture to the decision maker to identify the right resources within their data science activity. Practically the data scientist should be the one who can understand and moreover be involved with the data life cycle starting from inception > collection > operation > extraction > observation > preparation > description > prediction > prescription > Archival. Each of these aspects of data has a science behind it. An old team 'Jack of all trades' briefly defines this job description. A good data scientist essentially needs to be a good programmer, a good business/system/data analyst, a good statistician, one who can seamlessly visualize data, and is empowered with a vision to use and apply the necessary tools, techniques and methodologies in a scientific and applicable realistic way. Healthcare/Research environment is a complicated vertical when it comes to data, hence having domain knowledge is almost critical, complying with aspects of data governance such as patient privacy, consent, ethics etc.

Fundamentals of Implementing a National Clinical Registries Program.

A National registry program is a resource intensive initiative involving multiple stakeholders, multi-institutional/multi-role/multi-users collaborative effort, where various aspects starting from work culture, research culture, registry conceptualization, resource availability, data format, data storage/retrieval techniques, data sharing protocols, data/dataset standards, data quality etc. vary drastically between different institutions. The biggest challenge for a national program will be to map these aspects under a common umbrella to establish standards for operations/execution, policies and procedures, which means aligning the registry operations with the operative process of each institution at first, due to this only a handful initiatives are implemented with limited success, hence it is advisable to study such implementations in great details as a guideline to build a solid foundation for future national initiatives[1][2]. The idea goes around building a solid database for holding all clinical registries under a single repository, along with streamlining and generalizing the policies and procedures for any disease or medical device registry, in order to save infrastructure spending, streamlining, saving on management and operational costs and overheads.

Introducing a Research Management System to Speed Up and Streamline Clinical Research Activities.

Clinical Research is a complicated process within a research institution or a tertiary care hospital, almost all research project proposal needs to get an institutional review board (IRB) approval before any research activity takes place. IRB approval involves various processes, in form of sub-committees through which the proposal is reviewed. It is of utmost importance to understand the complete functioning of an IRB, in order to automate the various processes using a management system. The Research office entity forms the central body managing the IRB functions. It provides all sorts of administrative support as far as guidelines, documentation, communication and co-ordination is concerned; hence the research office forms the administrative wing of the IRB. Further the IRB has several sub-committees such as the Ethics Committee, Basic Research committee and the Animal Care and Use Committee. Each committee has a chairperson and several members from different specialty to cover all the aspects of research. Each committee may have its own process/workflow of approval, but usually the process of each committee is somewhat similar to each other. Apart from these workflows process the things that needs to be digitized would include researcher's profile, pre-award and post-award management, publication management, graduate student management and research analytics for the organization.

Developing an Evidence-Based Clinical Dataset for the Comprehensive Implantable Medical Device Registry (CIMDR).

Medical registries are in a need of a data set that is based on clinical evidence. In 2014, the Saudi Food and Drug Administration (SFDA) launched a plan to develop the national Comprehensive Implantable Medical Device Registry (CIMDR). One of the primary goals of the CIMDR is to develop a clinical -and population- based data set. The aim of this study is to report on the process of developing the data elements for the CIMDR. We used an iterative process of multi-stakeholder consultation over a two year period (2014-2016). The goal of the multi-stakeholder consultations was to build a dataset to address the need for device traceability, effectiveness, safety, and the recall of implantable medical devices. We investigated international and local standards for implantable medical device information capture, conducted a review of the literature, and consulted expert opinions in the development of the CIMDR dataset. The CIMDR data framework includes demographics, patient history, diagnosis, procedure information, and follow-up details for orthopedic and cardiac related implantable medical devices. Most of the dataset elements are logically validated with minimal free text entry to avoid human error and facilitate ease of entry. We use the International Classification for Diseases-Australian Modification as the standardized nomenclature for the CIMDR.

Designing and Developing a Multi-Center/Multi-Device National Registry for Implantable Medical Devices.

Designing, developing, and establishing the multi-device/multi-center Comprehensive Implantable Medical Device Registry (CIMDR) for Saudi Arabia is a strategic objective of the Saudi Food and Drug Administration (SFDA). The goal of the CIMDR is to capture all related clinical data along with device related information for implantable medical devices and study population-related outcomes. There is an immediate need in Saudi Arabia to establish the CIMDR to carryout device surveillance, gauge the efficiency and efficacy of various implantable medical devices, and track and recall implantable medical devices.In this work, we report on the development of the SFDA's CIMDR. We specifically focus on the project organization, five primary modules of the CIMDR, and development of the CIMDR through dynamic forms. We anticipate that the collected information in the CIMDR will be used by hospitals and the SFDA to improve patient safety relating to implantable medical devices in Saudi Arabia. Future development of the CIMDR will include a wide range of reporting and embedded analytical tools that will help researchers improve clinical standards and contribute to the research and development of implantable medical device technology.

Developing a Policy and Procedure Framework and Manual for a National Comprehensive Implantable Medical Device Registry in Saudi Arabia.

Policy and procedure manuals provide guidance on the operation and governance of medical device registries. In Saudi Arabia, the Saudi Food and Drug Authority (SFDA) has been developing and implementing a comprehensive national registry for implantable medical devices to facilitate the monitoring of device outcomes through post-market surveillance studies. To help guide the operations of this registry, the SFDA developed a policy and procedure manual. This paper reports on the design of the framework used to develop that manual over the course of one year (2015-2016), using a variety of literature sources, and working with medical device registry and health systems experts. The policy and procedure manual included five key principal level categories, which led to the subsequent creation of seven policies and 28 relevant procedures. The five principal categories were: Staff Engagement, Information Governance, Quality and Auditing, Research, and Reporting. The results of this work could be used to guide the development of policies and procedures for other implantable medical device registries.