Category: Blog

NLP, a branch of AI, aims at primarily reducing the distance between the capabilities of a human and a machine. As it beginning to get more and more traction in the healthcare space, providers are focusing on developing solutions that can understand, analyze, and generate languages can humans can understand.

Natural language processing (NLP) is the ability for computers to understand the latest human speech terms and text. It’s used in current technology to support spam email privacy, personal voice assistants and language translation applications. 

The adoption of natural language processing in healthcare is rising because of its recognized potential to search, analyze and interpret mammoth amounts of patient datasets. Using advanced medical algorithms, machine learning in healthcare and NLP technology services have the potential to harness relevant insights and concepts from data that was previously considered buried in text form. NLP in healthcare media can accurately give voice to the unstructured data of the healthcare universe, giving incredible insight into understanding quality, improving methods, and better results for patients.

Big data analytics in healthcare shows that up to 80 percent of healthcare documentation is unstructured, and therefore goes largely unutilized, since mining and extraction of this data is challenging and resource intensive. Without NLP technology, that data is not in a usable format for modern computer-based algorithms to extract. 

How Healthcare natural language processing work?

It uses specialized engines capable of scrubbing large sets of unstructured health data to discover previously missed or improperly coded patient conditions. Natural language processing medical records using machine-learned algorithms can uncover disease that may not have been previously coded, a key feature for making HCC disease discoveries.

EHRs and physicians don’t always get along well. The additional data input responsibilities create challenges, and can be frustrating. Researchers conclude, some physicians suffer from EHR burnout and threaten to retire from service early rather than suffer through the many clicks and screens required to navigate their EHR. Medical NLP is steadily proving to be a solution to this challenge since NLP healthcare tools can easily access and accurately interpret clinical documentation. Once the friction of healthcare technology is reduced, we can begin to appreciate more of the benefits of the technology and less of the daily frustrations. 

The accuracy of medical natural language processing

It goes up along with the volume of data available for learning. The more a medical NLP platform is used, the more accurate using artificial intelligence in healthcare gets, since it’s always learning, and in some cases, can be customizable. Some NLP healthcare systems offered by vendors advertise the ability to screen how the medical natural language processing would initially perform with a specific medical group. Then customize it next to the needs of that particular medical group. 

A distinct advantage natural language processing medical records offers is the ability for computer assisted coding to synthesize the content of long chart notes into just the important points. This could take organizations weeks, months, even years, to manually review and process stacks of chart notes from health records, just to identify the pertinent info. Natural language processing software for healthcare can scan clinical text within seconds and identify what needs to be extracted. This frees up physicians and staff resources to focus more on the complex matters and reduces the time spent on redundant administrative policy. When computers can understand physician notation accurately and process that data accordingly, valuable decision support can be obtained. These insights can be of significant use for future drug research and personalized medicine, which is good for patients and providers.   

Physicians don’t all “speak the same way”, and should always be aware that their notes and reports will likely be read by their work peers, patients and even computers, according to their organizations privacy policy. Avoiding non-standard language in note creation and management is extremely important. Most natural language processing healthcare engines are built to accommodate a wide variation of medical notation terminology. However, using uncommon acronyms can confuse NLP coding algorithms and other medical note readers.

NLP help in identifying Patients who need improved Care

Machine Learning and NLP tools have the capabilities needed to detect patients with complex health conditions who have a history of mental health or substance abuse and need improved care. Factors such as food insecurity and housing instability can deter the treatment protocols, thereby compelling these patients to incur more cost in their lifetime.

The data of a patient’s social status and demography is often hard to locate than their clinical information since it is usually in an unstructured format. NLP can help solve this problem. NLP can also be used to improve care coordination with patients who have behavioral health conditions. Both, Natural Language Processing & Machine Learning can be utilized to mine patient data and detect those that are at risk of falling through any gaps in the healthcare system.

Future of Healthcare natural language processing

In 2018 and 2019 the development to improve natural language processing healthcare data has proven challenging. If the NLP output displays too many suggested conclusions, or artificial conclusions that are incorrect, users will learn to ignore the intelligence and end up with a system that can reduce overall business productivity. NLP software for healthcare should center around data conclusions that have the least noise, and the strongest signal about what healthcare providers need to do. 

Healthcare natural language processing offers the chance for computers to do the things that computers need to do. To do the analytics, the HCC risk adjustment coding, the back office functions, and the patient set analysis, all without obstructing physician communication. 

NLP in healthcare is creating new and exciting opportunities for healthcare delivery and patient experience. It won’t be long before specialized NLP coding recognition enables physicians to spend more time with patients, while helping make insightful conclusions based on precise data. In the years to come, we’ll hear the news, and see the possibilities of this technology, as it empowers providers to positively influence health outcomes.

Learn more about NLP in healthcare:

https://www.sciencedirect.com/science/article/pii/S1532046418302016

https://healthtechmagazine.net/article/2020/05/language-processing-tools-improve-care-delivery-providers

What Is Big Data?

“Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.”

The insights gained from big data can allow businesses to solve problems that could not be tackled with traditional software or analytics. In healthcare, these new insights can help researchers gain a deeper understanding of data in order to improve the results of clinical trials, boost the productivity of healthcare professionals and improve revenues of the practices themselves.

Big data relies on three Vs: 

• Volume 
• Velocity
• Variety

Volume

This is the amount of data generated, such as through mobile apps, websites, portals and online applications.

Velocity

Velocity is how fast data is generated. 

Variety

This is the generation of both ‘structured data’ and ‘unstructured data’. Variety is about being able to translate data into specific categories.

How Healthcare Uses Big Data

Product Development

Discovering and developing new drugs and other health-related products takes an amazing amount of time and money.

Big data can help reduce the time involved in a number of different ways. This, of course, serves to reduce the costs involved.

Product development involves lengthy trial and error experimenting, which requires much time. Big data removes the guesswork allowing research and development companies to get results more quickly and thus develop more precise products faster.

Patient Outcomes

Big data improves patient outcomes because it helps doctors and other medical professionals be more efficient and accurate with their diagnoses and treatments. With the improved data analyzing methods big data provides, doctors can hope to find solutions to treat rare and serious conditions that would otherwise seem incurable because research can progress at a faster pace. 

Driving innovation

This is perhaps one of the largest uses of big data in healthcare. Without innovation, there would be no advancements in medicine at all. Big data will speed the rate at which new drugs can be discovered and the quality of care is improved.

The goal of efficiently using big data is to understand what is going on, identifying problems, and finding innovative solutions to them that will help reduce costs.

Who Benefits From Big Data Analytics?

Providers (Clinics, Hospitals)

The insights generated from big data analytics enables healthcare providers, such as clinics and hospitals, to improve patient care.

Patients

Patients will benefit from big data in healthcare more than anyone else. They can enjoy better overall care, live healthier lives, save money on insurance and so much more. 

Manufacturers

Big data analytics will enable device manufacturers to create better, innovative products to solve health issues. They will benefit from devices relevant to their needs.

Pharma

A 2013 study, published by Nature Review Drug Discovery, found that only 10% of medicines in development ever reach patients. With this in mind, big data in pharma will benefit from better research and development, resulting in more effective drugs and shorter production times. Pharma companies will also save on the costs related to drug development.

References:

• Big data in healthcare: management, analysis and future prospects | Journal of Big Data | Full Text (springeropen.com)
• Big data analytics in health informatics | International Journal of Development Research (IJDR) (journalijdr.com)
• Big Data, Big Problems: A Healthcare Perspective – PubMed (nih.gov)

Healthcare IT integration is a combination of IT and healthcare sector and involves the application of latest IT solutions to monitor people’s health, perform secured exchange of their electronic data and provide economical healthcare solution. The purpose of integrating health systems is to provide seamless services; make them more accessible, easy to understand and use; and lead to better overall patient health.

The major factors that will accelerate the growth of the healthcare IT integration market are high healthcare costs, government initiatives to curb this rising cost and the growing demand to incorporate IT in the healthcare domain.  Healthcare IT Integration Market is valued at 3.15 USD Billion in 2019 and expected to reach 6.21 USD Billion by 2026 with the CAGR of 10.2% over the forecast period.

The healthcare IT integration market has been categorized on the basis of product and services, and application. The products in healthcare IT integration market include interface/integration engines, media integration solutions, medical device integration software, and other integration tools. Based on the services provided, the market is segmented into implementation services, support and maintenance services, and training services.

Based on application, the market is segmented into hospital integration, clinic integration, lab integration, radiology integration, medical device integration and others. Due to the multiple applications of IT integration in hospitals, that encourage value based healthcare reimbursement, the hospital integration segment holds the largest share and is expected to witness growth during the forecast period.

MARKET SEGMENTATION

By Product, Services & Application

• By Product
  • Interface/Integration Engines
  • Media Integration Solutions
  • Medical Device Integration Software
  • Other Integration Tools

• By Services
  • Implementation services
  • Support and maintenance services
  • Training services

• By Application
  • Hospital integration
  • Clinic integration
  • Lab integration
  • Radiology integration
  • Medical devices integration

What are the benefits of Medical Devices Integration Software?

The top four reasons to consider implementing such software into the hospital’s work.

1. Integrated workflow. By integrating medical devices the hospital receives an efficient, easy, and understandable workflow. All the sections of the hospital are connected, and due to the web-based system, they can be easily controlled.
2. Eliminating the manual work with health records and clinical data. Now the software for integration is able to collect and analyze the data automatically. The medical personnel will receive clear and processed information, probably even collected and summed up from more than one device at a time. So the process goes way faster increasing data quality and eliminating all the possible errors due to human factors.
3. Remote control. Now doctors can watch over the health status of each patient even if they aren’t physically at the hospital. It can be possible due to the cloud-based online access to the information and the personal cabinet.
4. Web-based storage. The received data from all the medical devices now will be filtered, stored, and secured online. This means that there is no more struggling with searching for the required patient’s data through all the devices and the system will collect everything automatically and send the data directly to the place of storage.

Read more about the integration of Healthcare IT:

https://jamanetwork.com/journals/jama/fullarticle/645857

Benefits of Healthcare Information Technology | CCHIT

While the health IT sector has made a big impact in terms of facilitating cooperation between electronic health records (EHRs). Interoperability requires a collaborative environment around healthcare technology infrastructure, data standards, and permissions, among other things. To simplify healthcare data exchange, health IT vendors and providers should work closely with support from governmental agencies to make continual progress on this path to true interoperability. The four basic functions required for interoperability: data gathering, data reception, data distribution, and data integration.

Five keys to simplifying interoperability in healthcare.

1. Wider Adoption of Data Standards

The standards for digital exchange of data to achieve true interoperability. HL7 has gained widespread adoption, yet room for interpretation means that there’s significant variance in how these standards are implemented. FHIR a newer specification developed by HL7, makes some strides yet shares some of the same concerns that exist with the HL7 standards. For instance, vendors may not implement all FHIR APIs, or they may not implement the complete APIs either scenario prevents true interoperability. The goal must be to shift from isolated data and participate in the industry process of building an interoperable healthcare ecosystem.

2. Choosing the Right Health Information Technologies

Healthcare organizations that continue to use inefficient or outdated technologies for data integration are going to find it difficult to make the transition. Their tech interface might be incompatible with the cutting-edge cloud technologies, or they may discover that their system simply does not support the modern data formats. These issues are common and require a reliable and advanced solution.

3. Improving EHR Integration for Better Point of Care Solutions

EHR integration is one of the most important use cases in healthcare interoperability. Interoperability can avoid the need for a clinician to work with diverse interfaces to obtain the data they need. According to health IT experts, doctors are far more likely to modify their treatment protocols if they are provided with relevant substantiating data right at the point of care.

4. Bringing More Uniformity in State Privacy Laws

The process of healthcare interoperability can go to the next level if there is more harmony between the rules and regulations related to healthcare data privacy. To achieve nationwide interoperability for the best possible patient care, providers in one state should be in a position to exchange vital health information with providers in another state.

5. Augmenting Patient Matching Capabilities

Healthcare interoperability gets further complicated if the patient matching technologies are not up to the mark. With unreliable patient matching systems, even if the healthcare providers manage to streamline other issues, they would still fall short of their objectives if the data is not attributed to the appropriate patients on the other side.

 

Read more about Interoperability in healthcare:

Artificial Intelligence is the intelligence shown by machines that can be helpful to perform several tasks using sentiment analysis and Natural Language Processing (NLP). This technology allows machines to learn on their own from past data and the given information, make sense of it, and use this information to do various business tasks. AI is a superset of Machine Learning and Deep Learning, and these technologies have their own sets of responsibilities while equipping machines.

Role of AI in Healthcare

AI is being leveraged to deploy efficient and precise inventions that will help take care of patients suffering from these diseases and hopefully find a cure for them. AI provides several advantages over traditional methods of analytics and making clinical decisions. AI algorithms make the systems more precise as they get the opportunity to understand training data, which furthers helps humans get unprecedented insights into treatment variability, care processes, diagnostics, and patient results. 

Pros and Cons of Artificial Intelligence in Healthcare

While there are several benefits of this technology in the field of healthcare, it has some flaws as well

Pros	Cons
Improved diagnosis	Complications in learning AI
Serves rural communities better	A difficult change to adapt to
Better clinical decisions	Requires human assistance
Streamlines several processes	Requires the implementation of the correct AI platform

 

What are the applications of artificial intelligence systems in healthcare?

1. Detect signs of diabetic retinopathy

AI diagnostic system has been developed to autonomously analyzes images of the retina for signs of diabetic retinopathy.

2. Breast density via mammography

Monitor breast density via mammography to support accurate decisions in breast cancer screening.

The technology uses AI to assess breast density in order to identify patients that may experience reduced sensitivity to digital mammography due to dense breast tissue.

3. Support in Clinical Decisions

The assistance of Natural Language Processing (NLP) makes it more convenient for doctors to narrow down all relevant information from patient reports. 

Artificial Intelligence holds the ability to store and process large sets of data, which can provide knowledge databases and facilitate examination and recommendation individually for each patient, thus helping to enhance clinical decision support.

4. Enhance Primary Care and Triage through Chatbots

Artificial Intelligence assists in enabling smooth flow and automation of primary care, allowing doctors to stress over more crucial and dire cases. 

Saving money on avoidable trips to the doctor, patients can benefit from medical chatbots, which is an AI-powered service, incorporated with smart algorithms that provide patients with instant answers to all their health-related queries and concerns while also guiding them on how to deal with any potential problems. 

5. Robotic Surgeries

AI and collaborative robots have revolutionized surgeries in terms of their speed, and depth while making delicate incisions. Since robots don’t get tired, the issue of fatigue in the middle of lengthy and crucial procedures is eliminated.

AI machines are capable of employing data from past operations to develop new surgical methods. The preciseness of these machines reduces the possibility of tremors or any unintended or accidental movements during the surgeries. 

6. Virtual nursing assistants

AI systems facilitate virtual nursing assistants that can perform a range of tasks from conversing with patients to directing them to the best and effective care unit. Many AI-powered applications of virtual nursing assistants presently enable more regular interactions between patients and care providers between office visits to avoid any unnecessary hospital visits. Also there is a system can even facilitate wellness checks through voice and AI.

7. Aiding in the accurate diagnosis

AI has the capacity to surpass human doctors and help them detect, predict, and diagnose diseases more accurately and at a faster rate. Likewise, AI algorithms have proved to be not only accurate and precise at specialty-level diagnostics, but also cost-effective in terms of detecting diabetic retinopathy. 

 

Interoperability Definition 

HIMSS (Healthcare Information and Management Systems Society) defines interoperability as:

The extent to which systems and devices can exchange data, and interpret that shared data. For two systems to be interoperable, they must be able to exchange data and subsequently present that data such that it can be understood by a user.

In simple terms it means various healthcare systems can communicate seamlessly with each other making medical data universally available.

Interoperability in Healthcare Data: A Life-Saving Advantage

When health system clinicians make care decisions based on their organization’s EHR data alone, they’re only using a small portion of patient health information. Additional data sources such as health information exchanges (HIEs) and patient-generated and reported data round out the full picture of an individual’s health and healthcare needs. This comprehensive insight enables critical, and sometimes life-saving, treatment and health management choices. To leverage the data from beyond the four walls of a health system and combine it with clinical, financial, and operational EHR data, organizations need an interoperable platform approach to health data. The Health Catalyst® Data Operating System (DOS™), for example, combines, manages, and leverages disparate forms of health data for a complete view of the patient and more accurate insights into the best care decisions.

Four Levels of Interoperability

• Foundational (Level 1): Establishes the inter-connectivity requirements needed for one system or application to securely communicate data to and receive data from another.
• Structural (Level 2): Defines the format, syntax and organization of data exchange including at the data field level for interpretation.
• Semantic (Level 3): Provides for common underlying models and codification of the data including the use of data elements with standardized definitions from publicly available value sets and coding vocabularies, providing shared understanding and meaning to the user.
• Organizational (Level 4): Includes governance, policy, social, legal and organizational considerations to facilitate the secure, seamless and timely communication and use of data both within and between organizations, entities and individuals. These components enable shared consent, trust and integrated end-user processes and workflows.

What is Health Information Exchange and Data Sharing?

Health information exchange, or HIE, provides the capability to electronically move clinical information among disparate healthcare information systems and maintain the meaning of the information being exchanged. The goal of health information exchange is to facilitate access to and retrieval of clinical data to provide safe, timely, efficient, effective and equitable patient-centered care. HIE can also be used by public health authorities to assist in the analysis of the health of populations.

The benefits of interoperability in health care

Providers and plans are likely to see the highest ROI through the following:

• Reduction in administrative costs 
• Increased efficiency of care delivery 
• Reduction in the total cost of care 
• Increased revenue and growth

Reduction in administrative costs

Interoperability in health care can enable both provider and plan organizations to reduce or redeploy FTEs away from time-consuming manual processes that often do not create value to tasks that can directly reduce health care costs and improve quality.

Increased efficiency of care delivery

Radical interoperability in health care allows clinicians access to real- or near-real-time data wherever care is being delivered. These capabilities can also enable clinicians to change how care is delivered, both in terms of where care is delivered and who delivers it, in order to increase the number of patients receiving care.

Reduction in the total cost of care

While health plans have always strived to reduce the amount of health care services utilized, providers in value-based care (VBC) reimbursement models are now incentivized to take similar steps.

Increased revenue and growth

The organizations leveraging technology to make interoperability in health care happen faster will likely acquire and retain patients most effectively. Plans and providers are shifting their focus to the health care consumer, who can drive revenue and growth.

 

References:

https://www2.deloitte.com/us/en/pages/life-sciences-and-health-care/articles/interoperability-in-healthcare.html

Interoperability in Healthcare         

Most of the radiology facilities need some workflows/functionalities those help providing better patient service, evaluate/enhance reporting quality or enhance learning process.
These workflows/functionalities include:

• Teaching Files
• MDT Meetings
• Peer Review
• Critical Results Management
• Patient Engagement
• Imaging Mobility

The question is whether to have these workflows/functionalities either:
   – Built-in PACS/RIS solutions,
OR
   – Have them as independent (vendor natural) solutions (not part of the PACS/RIS solutions, yet seamlessly integrated to them)? 

Having them as PACS native built-in functions may guarantee the integration within the PACS workflow. However, there are FIVE reasons why having these workflows/functionalities independent from the PACS would be much more useful:

1. Customization
The level of customization would be higher and the features set would be larger resulting in fulfilling larger base of end-users.

2.  Data migration
Migrating data related to these workflows/functionalities as modules built in the PACS to another PACS system would be very difficult or even impossible. So having them as independent solutions would be much more useful and efficient as it would be only a matter of integrating these solutions to the new PACS system to continue using them while maintaining the whole history, old data and users’ familiarity with these solutions.

3.  Adding new features
Adding new features or response to change requests would be much faster and more flexible as this would be having a new release from that independent solution only. This is not the case when adding a feature (for example in MDT workflow) included in the PACS system, which requires having a whole new release from the PACS system (much harder and longer process).

4.  Integration to multiple PACS systems
It is very difficult to integrate the teaching files, MDT or peer review included in the PACS/RIS to another PACS/RIS system (either in the same hospital or in another hospital) while this is very easy to do if any of these functionalities is an independent solution.

5.  Regional solutions
As the need to have regional MDT or teaching files solutions is increasing to overcome the shortage of resources or sharing knowledge & expertise, the need to have these workflows/functionalities in a regional level is increasing, as this would be very hard to be done with these functionalities built-in the PACS/RIS with the need to add more features and manage both local/regional activities and integration to multiple PACS/RIS/EMR in different hospitals within that region.

Click here to learn how Rosenfield’s solutions enhance the medical imaging workflows.

What Is Peer Review?  

Peer review is a continuous, systematic, and critical reflection and evaluation of physician performance using structured procedures. Peer review is an evaluation by a colleague, who could be of the same or a different discipline working in a practice or hospital unit.

Why Peer Review?        

Quality gaps exist in all medical specialties, including radiology. Mistakes in radiology are common at a rate comparable with other medical errors. It is crucial for the radiology community to find ways to mitigate and prevent errors that potentially cause harmful health outcomes. Radiologists have the privilege of self-governance, with the responsibility of mutual accountability for quality. Thus, it is our obligation to ensure ourselves of optimal competency.

How to Perform Peer Review   

Most institutions have at least one method of peer review, not limited to retrospective medical record review. The systems are commonly conducted in committees, and important decisions are made by committee consensus.

What to Measure

The ideal measures should be evidence-based and agreed-on standards that are easily reproducible and represent good attributes for the individual radiologist’s work. In addition, measurement needs to occur in sufficient numbers to allow a meaningful statistical evaluation.

How to Measure

There are several methods to assess radiologists’ technical performance. The most commonly used and fundamental to peer review is case review. Case review is a professional review of submitted cases found to contain potential errors detected by radiologist peers, colleagues, or other medical professionals. Case review is a reactive process because performance is assessed and documented only when a discrepancy arises and is reported. Proactive review, on the other hand, is different in that the review occurs in a blinded manner. The cases are randomly assigned for double interpretation or assessment of agreement by separate radiologists.

This technique is used in the iCode Peer Review system to assess interpretive agreement of prior imaging studies. iCode Peer Review is a vendor neutral solution enhances the radiology reporting quality and facilitate improved quality of patient care. The solution complies with ACR peer review standards (score cards 2009 and 2016 editions).

Peer review is performed during the routine interpretation of current images by evaluating prior studies and interpretation. The current reviewing radiologist assigns a score based on a standardized 4-point rating scale regarding the level of quality concerns to the prior interpretation performed by the original interpreting radiologist. It is the solution to meet the fourth requirement for maintenance of certification, which is evaluation of performance in practice.

For more more information about iCode Peer Review, please visit below page:

www.rosenfieldhealth/peer-review

Source: https://www.ajronline.org/doi/full/10.2214/AJR.11.8143

MDT stands for Multidisciplinary Team Meeting. It provides an opportunity for Consultants to discuss complex cases. The principle of an MDT meeting is to discuss patients with other colleagues from the same discipline and where indicates from other specialists in order to ensure the patient is receiving the correct advice and that the treatment is suitable to their individual needs.

The MDT comprises specialist doctors and nurses, who meet regularly to establish that every patient’s diagnosis is correctly made, based on blood and tissue samples, X-rays and scans.

The MDT will then discuss and recommend the best form of treatment based on local and national guidelines and on each individual patient’s circumstances.

MDT meetings may be held at your local hospital, or regionally by video conference to share expertise.

Who attends MDT meetings?

The following core members of the MDT are required to attend meetings every week or fortnight.

Consultant Haematologists and Specialist Registrars who have instigated investigations to obtain a diagnosis and are responsible for the treatment of patients.

Consultant Radiologists who review and report scans and X-rays to confirm diagnosis and who can advise on further testing, monitoring or management.

Consultant Histopathologists who specialise in blood and bone marrow diseases, who report their findings after examining and testing tissue samples to confirm diagnosis.

Consultant Oncologists who recommend and provide radiotherapy treatment when required.

Advanced Nurse Practitioners who ensure that every patient has a named Key Worker allocated and contribute to discussion of care of patients based on holistic needs assessment.

Research Nurse who ensures that any appropriate patients who might benefit from being entered into a clinical trial are discussed.

MDT Coordinator who organises the meetings and ensures the appropriate information is available for cases to be discussed and documented.

Other members of the MDT may not necessarily attend meetings, but are available to provide advice to the MDT and additional care to patients.

These include:

• Specialist Palliative Care Service
• Surgical Services
• Blood and Marrow Transplant (BMT) Service
• Immunologist
• Clinical Psychologist/Psychiatrist
• Occupational Therapist
• Dietitian
• Physiotherapist
• Cancer Pharmacist
• Teenage and Young Adult Cancer Service

Patients may have their cases referred to the MDT at any time, when significant changes occur and further treatment options need to be considered.

The MDT will take into account patients’ views and individual circumstances. The conclusions and recommendations of the MDT are shared with patients at clinic appointments.

Need a comprehensive solution to manage MDT meetings? Please click here

 

References:

• Oxford University Hospital – NHS Trust
• London Bridge Hospital

An important term spreading through the healthcare interoperability world is HL7 FHIR, pronounced as “fire.” Here are the Top 5 things you should know about this standard that will help solve many current issues in interoperability:

• FHIR stands for Fast Healthcare Interoperable Resource.
• FHIR combines the best features of HL7 V2, HL7 V3, and CDA, while leveraging the latest web service technologies.
• The design of FHIR is based on RESTful web services. This is in contrast to the majority of IHE profiles which are based on SOAP web services. With RESTful web services, the basic HTTP operations are incorporated including Create, Read, Update and Delete.
• FHIR is based on modular components called “resources,” and these resources can be combined together to solve clinical and administrative problems in a practical way. The resources can be extended and adapted to provide a more manageable solution to the healthcare demand for optionality and customization. Systems can easily read the extensions using the same framework as other resources.
• In March 2012, the FHIR specification was transferred from Grahame Grieve, creator and architect, to HL7 International and was made freely available. Grieve started work a year prior in response to outcomes from the HL7 Fresh Look task force. FHIR is still being developed by HL7, but the first Draft Standard for Trial Use (DSTU) should be available by the end of 2013.