Radiology Events and Learning Meetings

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Radiology Events and Learning Meetings

RCR Radiology Events and Learning Meetings "REALM"

A positive culture to develop learning is vital to improve outcomes and comply with recommendations of the Francis Report.

As radiologists are constantly striving to improve the standards of service they provide to patients with a culture of learning, self-reflection and personal development.

Humans will always make errors and radiologists are no different. They also have moments of brilliance.

What is REALM Meetings:

It is periodic meetings held for reviewing and learning from excellence, discrepancies and adverse events can provide evidence of reflective practice, improve outcomes and, if performed in a supportive learning environment, can contribute to the evidence for providers and users of the safety of a service.

Structuring the learning to help identify contributing factors can also help inform the organisation of potential trends that can be addressed to mitigate against recurrence of discrepancies, to empower excellence and to contribute to the enhancement of patient safety. 

Documentation of reflection and learning outcomes of the meeting should be anonymous to encourage submission and enhance learning.

The RCR has published the standards & guidelines of the REALM so that it helps in continuous learning & enhance patient service.

More details about REALM & its published RCR standards in below link

  1. https://www.rcr.ac.uk/system/files/publication/field_publication_files/bfcr201-standards-for-radiology-events-and-learning-meetings.pdf

Learning From Discrepancies

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Learning From Discrepancies

Does learning from discrepancies have to be painful? Does the lead of clinical errors can be turned in to the gold of useful educational tools?

The lead of clinical errors can be turned into the gold of useful educational tools. An awareness of errors and their underlying causes can potentially increase the diagnostic performance and reduce individual harm.

Holding meetings to discuss discrepancies shows how difference of opinion between radiologists can be the gold of useful educational tools and improve the healthcare cycle.

Radiological imaging is an essential part of patient management. Despite significant technological developments, a radiological investigation is rarely definitive on its own, leading to discrepancies between radiological impressions and the ultimate outcome. Moreover, radiology reports, like all human endeavors, may contain errors or misunderstandings.

Definition of Discrepancy

   Discrepancy refers to a reasonable difference of opinion between radiologists about a finding or diagnosis. It differs from error because discrepancies can be justified based on a range of scientific data, such as clinical information, laboratory results or radiological patterns. A reporting discrepancy occurs when a retrospective review, or subsequent information about patient outcome, leads to an opinion different from that expressed in the original report.

 The term “error” is described as no uncertainty about the correct finding, with no possibility for dispute or disagreement, while the word “discrepancy” stands for justifiable differences of opinion between colleagues.

Diagnostic error is a condition that could harm the patient, with no acceptable cause and no scientific data for defense, approved by all experts in this field.

the ‘lead’ of clinical errors can be turned in to the ‘gold’ of useful educational tools. Interrogating the whole database periodically can enable a more constructive, wider view of the meeting itself, identify recurrent deficiencies in practice and point to where the need for continuing medical training is greatest. A regular, non-judgmental, anonymous, inclusive educational cases meeting is vital.

More details in below links

  1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3609674/
  2. https://link.springer.com/article/10.1186/s13244-021-00986-8
  3. https://www.rcr.ac.uk/sites/default/files/publication/BFCR(14)11_LDMs.pdf
  4. https://www.researchgate.net/publication/311500275_Error_and_discrepancy_in_radiology_inevitable_or_avoidable
  5. https://www.radiology.ie/images/uploads/2012/01/Errors-Discrepancies-in-Radiology-Frequency-Causes-Prevention-and-Management.pdf

Communicating Critical Results (unexpected findings) in Radiology

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Communicating Critical Results (unexpected findings) in Radiology

Communicating Critical Results (unexpected findings) in Radiology

Critical findings in imaging are the findings from imaging examination of a patient, which indicates that condition of the patient may be critical or even life-threatening. Whenever such critical findings are made, it is necessary that the treating clinician of the patient is informed immediately so that required interventions can be carried out in time. To be able to do so, a hospital needs to have a well-developed process of ‘identification and urgent communication of critical findings in Imaging’.

Such process should be able to achieve following objectives.

  1. Critical findings are identified while carrying out (or immediately after) the imaging examination
  2. Critical findings are not missed
  3. Non-critical findings do not get identified as critical
  4. The treating doctor of the concerned patients gets to know about the critical findings on an urgent basis

Defining critical findings: 

Critical findings must be defined by the hospital/Imaging centers for a uniform understanding of all staff in the department. This is important also because other terminologies such as critical values, panic values, panic findings etc. are also used for the same situation.

A good definition of ‘critical imaging finding’ is, ‘A finding in imaging examination that requires immediate or urgent communication with the treating doctor of the patient. These finding reflect condition of the patient that are life-threatening or can cause life-long disability.

It shall also be clarified that critical findings are not same as abnormal findings. There can be findings that are indicative of a disease or health problem but unless the condition is serious enough and needs urgent intervention, they may not be considered ‘Critical’.

Communication of critical finding: 

As soon as the finding is confirmed to be critical, the treating physician of the concerned patient shall be informed through the quickest mean possible. This communication shall preferably be done by the Radiologist, however, if the he/she is not available at the time, the junior doctor or the staff who did the examination must directly inform the physician.

In case physician is not immediately available to receive the communication, the resident doctor on duty or the nurse in-charge of the ward must be informed about the critical finding.

The verbal/telephonic communication shall be complete and incorporate following information

  • Name of the patient
  • Imaging examination performed
  • Finding

What to consider in the process:

Consider the following recommendations:

  • Create a critical results list and define different levels of criticality.
    • Involve radiologists and emergency physicians in the creation of critical findings lists.
    • Avoid creating a list that is too difficult to use because of its length and detail.
    • Take into consideration the prevalence of findings based on the patient population.
    • Analyze and revise the list regularly to reflect changing practice patterns and disease incidence.
  • Define acceptable methods of communication for each category of critical results.
    • Include clear identification and read-back directions for spoken transactions, for example:
      • Identify yourself.
      • State the emergency nature of the call.
      • Verify the identity of the person receiving the result.
      • State the name of the study and the critical results.
      • Request a read-back of the patient’s name and critical results.
      • Document the interaction, including the date, time, and names of both parties.
    • Identify where spoken critical result communications should be documented (e.g., PACS, final radiology report, or log).
  • Define by whom and to whom critical results should be reported.
  • Outline the information that should be included in documentation of direct critical result communication, including:
    • “Critical result” as a lead off to the documentation
    • The name of the person to whom the results were reported
    • The date and time
    • The method of communication
    • What was discussed
    • An assertion that the communication was understood
  • Identify who should receive the results if the ordering clinician is not available.
  • Monitor and evaluate critical results communication procedures.
  • Determine whether critical results are being communicated in the time frames designated in the policy.

More details in below links

  1. American College of Radiology. ACR Practice Parameter for Communication of Diagnostic Imaging Findings. (Resolution 11) 2014.
  2. Alyssa Martino. “Getting the Message: How Can Radiologists Best Communicate Critical Test Results?” ACR Bulletin. 2015, 3.
  3. David L. Weiss, et al. “Radiology Reporting: A Closed-Loop Cycle from Order Entry to Results Communication.” Reference Guide in Information Technology for the Practicing Radiologist. 2013. DOI: 10.1016/j.jacr.2014.09.009
  4. The Joint Commission. “National Patient Safety Goals for the Hospital Program.” National Patient Safety Goals. Goal 2, NPSG.02.03.01. Effective January 1, 2021.

How the medical field is benefiting from AI in 2021 and beyond

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How the medical field is benefiting from AI in 2021 and beyond

How the medical field is benefiting from AI in 2021 and beyond

  • AI has the ability to analyze big data sets pulling together patient insights and leading to predictive analysis.
  • By 2030, AI will access multiple sources of data to reveal patterns in disease and aid treatment and care.
  • Healthcare systems will be able to predict an individual’s risk of certain diseases and suggest preventative measures
  • AI will help reduce waiting times for patients and improve efficiency in hospitals and health systems.
What is Artificial Intelligence in Healthcare?

Artificial Intelligence (AI) is commonly known for its ability to have machines perform tasks that are associated with the human mind like problem solving. However, what’s less understood is how AI is being used within specific industries, such as healthcare.

The healthcare industry continues to evolve as machine learning and AI in technology become more prevalent. Insider Intelligence reported that spending on AI in medicine is projected to grow at an annualized 48% between 2017 and 2023. 

Machine learning has the potential to provide data-driven clinical decision support (CDS) to physicians and hospital staff paving the way for an increased revenue potential. Deep learning, a subset of AI designed to identify patterns, uses algorithms and data to give automated insights to healthcare providers.

Benefits of AI in Healthcare & Medicine

Integrating AI into the healthcare ecosystem allows for a multitude of benefits, including automating tasks and analyzing big patient data sets to deliver better healthcare faster, and at a lower cost. 

According to Insider Intelligence, 30% of healthcare costs are associated with administrative tasks. AI can automate some of these tasks, like preauthorizing insurance, following-up on unpaid bills, and maintaining records, to ease the workload of healthcare professionals and ultimately save them money. 

AI has the ability to analyze big data sets pulling together patient insights and leading to predictive analysis. Quickly obtaining patient insights helps the healthcare ecosystem discover key areas of patient care that require improvement. 

AI-powered predictive care

AI and predictive analytics help us to understand more about the different factors in our lives that influence our health, not just when we might get the flu or what medical conditions we’ve inherited, but things relating to where we are born, what we eat, where we work, what our local air pollution levels are or whether we have access to safe housing and a stable income. These are some of the factors that the World Health Organization calls “the social determinants of health” (SDOH).

In 2030, this means that healthcare systems can anticipate when a person is at risk of developing a chronic disease, for example, and suggest preventative measures before they get worse. This development has been so successful that rates of diabetes, congestive heart failure and COPD (chronic obstructive heart disease), which are all strongly influenced by SDOH, are finally on the decline.

References

https://journals.sagepub.com/doi/10.1177/0840470419850438

https://www.nature.com/articles/s41467-021-20910-4

https://healthitanalytics.com/news/top-12-ways-artificial-intelligence-will-impact-healthcare

FHIR Next Generation Communication Standards in Healthcare

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FHIR Next Generation Communication Standards in Healthcare

FHIR Next Generation Communication Standards in Healthcare

Interoperability is the highest-trending topic in healthcare IT today

Fast Healthcare Interoperability Resources, or FHIR, is a long-awaited interoperability rule that will enable seamless, on-demand information exchange of clinical records among providers and data systems and will result in coordinated, cost-efficient care.

With the COVID-19 outbreak, healthcare data interoperability has become an increasingly pressing concern for healthcare organizations. Healthcare experts have long proposed that easy sharing of clinical records among providers and data systems would result in coordinated, cost-efficient care. Patient rights advocates have also encouraged secure data sharing to improve patients’ and their providers’ access to critical patient information. As a result, there’s a great deal of attention towards the data standards that will enable seamless, on-demand information exchange, such as FHIR, or Fast Healthcare Interoperability Resource.  

FHIR is one of the most popular protocols for connecting disparate data systems and sharing data in a secure manner. Since the interoperability rule released by the Office of National Coordinator of Health IT (ONC) and Centers for Medicare and Medicaid Services (CMS) states the adoption of FHIR Release 4 for API-based data exchange, it’s important to get answers to certain important questions. 

Why Was FHIR Created?

FHIR is better than current standards because:

  • Faster, easier implementation and interoperability
  • Free, unrestricted use
  • Human and machine-readable data formats in the same resources
  • Supports RESTful architecture
  • Emphasis on web technologies
What FHIR Means for Healthcare IT Monitoring?

Using popular, well documented web standards to exchange healthcare data will lower the barrier-to-entry for healthcare providers that want to find new ways to monitor and derive insights from that clinical and patient data. If you are interested in improving patient care and satisfaction through technology, you should take a good look at FHIR. From the looks of things, FHIR promises an easier way to develop greater interoperability and mechanisms for data sharing in healthcare. The current inability for electronic systems to speak the same language to one another and to efficiently and correctly transmit information to be interoperable is among the most pressing issues facing health care stakeholders today.

What is FHIR 4 and how can it help health IT interoperability?

FHIR surfaced in healthcare almost five years ago as a Draft Standard for Technical Use (DTSU) that leveraged existing, well-established, web-based standard concepts altered to fit clinical data exchange. The modern interoperability standard is an essential component of health information exchange in modern health IT and in early 2019, had the 4th release, or R4. 

FHIR 4 builds upon the legacy of DTSU2 and v3, and like previous versions, this version lets data travel in discrete pieces. Along with RESTful application programming interfaces (API), the version also includes additional stability for several of the standard’s elements, which will result in fewer challenges in leveraging it. 

FHIR 4 is a balloted and passed standard that would be a normative standard, which means the specification is considered ready for use and stable, and further FHIR versions will be required to be backward compatible with the R4 components. This would make building apps off this version easier to be supported for the foreseeable future. 

References:

https://datascience.nih.gov/foa/fast-healthcare-interoperability-resources-fhir-standard

https://www.managedhealthcareexecutive.com/view/fhir-interoperability-what-it-and-why-should-we-care

https://www.hl7.org/fhir/healthcareservice.html

Healthcare Data Security: How to Protect Patient Information?

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Healthcare Data Security: How to Protect Patient Information?

4 Ways to Improve Patient Data Security

IT solutions in the healthcare industry have already simplified life for both doctors and patients. However, the issue of patients’ data privacy remains a sore point.

Below are four useful tips to assist health systems and hospitals in reinforcing and better enhancing the safety of their healthcare data. These security practices and measures aim to help these organizations address imminent risks to data privacy, keep pace with continually evolving threats and also effectively protect patients’ personal information.

1.    Educate Personnel on Security Best Practices

The “human element” remains among the biggest security threats across multiple industries, healthcare included.

 Security awareness training not only equips healthcare staff with the essential knowledge necessary for making wise decisions, but it also ensures that they use appropriate precautionary measures when handling patient data.

2.    Routine Risk Assessment

Experts recommend that healthcare organizations perform risk assessment sessions on a regular basis to determine the vulnerabilities of their systems. By identifying weak links within their data security systems, healthcare organizations can effectively fix any issues before they arise.

It is a good idea for healthcare providers to even consider conducting these assessments more than once yearly-perhaps monthly or quarterly for maximum safety.

3.    Access Control: Restricting Access to Applications and Data

Controlling access to private health records is also another crucial way of enhancing the overall security of patient data. How can organizations do this? By ensuring that only the certified and essential personnel are granted access to sensitive data. This reduces the risks of data breaches and theft.

When you implement access control, you can effectively reinforce healthcare data security. This is by restricting access to specific applications and sensitive patient information only to the individual needing access to carry out their duties. Access restrictions require user authentication, and this ensures that authorized users only gain access to protected healthcare data.

Multi-factor authentication, which mandates for users to verify their identities through two or more methods of validation, is among the most-recommended approaches you can use.

Furthermore, whenever possible, healthcare organizations can engage this essential personnel in their two-factor authentication. You can incorporate thumb scanning and retina scanning technology or subsequently adopt a mobile authentication system for all the staff with access to sensitive records and then log them into the security system.

Some security systems can even allow you to create distinct passwords and logins for every staff member you would like to allow access to confidential data. By diversifying access keys in such a manner, these organizations make it more difficult for any hackers or outside individuals to crack and breach their code.

4.    Encryption, Encryption, Encryption!

Encryption is undoubtedly among the most effective methods of data protection across all industries, not just in healthcare. Data, either in-transit or stored, needs to be encrypted on every device within the system. This includes computers, cell phones, USB drives, tablets, and laptops.

Encrypting data allows healthcare organizations and stakeholders to minimize their susceptibility to data breaches and cyber-attacks effectively. Encryption ideally makes it harder (virtually impossible) for a hacker to decipher personal patient data even if they manage to breach and subsequently gain access to the information. Extraction of data from unencrypted stolen devices can amount to millions of dollars in losses.

Summary

While taking a sophisticated, multi-faceted security approach and measures may appear exhausting, where valuable and sensitive patient healthcare information is at risk, these additional security measures can guarantee protection.

To effectively keep up with the continually emerging security risks, it is crucial for healthcare providers to improve their data security and protection with these few approaches.

Learn more about data security in healthcare:

https://www.hipaajournal.com/secure-patient-information-phi/

https://www.nejm.org/doi/full/10.1056/NEJMp1215258

https://academic.oup.com/jamia/article/14/2/239/863754

 

 

Data Migration in Healthcare

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Data Migration in Healthcare

Data Migration in Healthcare

Healthcare data migration from legacy system is getting complex and critical with advent of RIS, CVIS, EHR, EMR, CPOE and other data driven information systems employed in today’s healthcare industry to deliver best medical care.

Healthcare industry is creating massive volumes of structured and unstructured data, digital imaging and video content each year. Healthcare data migration is not a simple process of extracting data from one source and transfer to new source. Migrating data to new EHR (Electronic Health Record) systems require error free data migration expertise and proven technology.

PACS Data Migration and Implementation

Whether implementing a PACS for the first time or changing PACS software or adding new interfaces with multiple systems within healthcare network impact legacy data archive that may not be accessible or read from backup media. When managing PACS changes, it’s important to find a provider with PACS data migration expertise to help you in extracting data from old (legacy system), validate and transfer to new system and also maintain uninterrupted patient care.

Healthcare Data Storage & Management

Planning for upgrading your healthcare network data storage for short term/ long term strategy to a highly scalable data storage or storage vendor changes may result in data loss and downtime. Demand for massive data storage from the healthcare industry is on the rise with expectation of a highly manageable system at reduced costs means frequent vendor changes.

In specific to healthcare IT, there are different migration in terms of databases, standards, and compliance, such as the following:

  • Migration from ICD9 to ICD10
  • CCD to CCDA Standard
  • Impact Analysis
  • ICD 9 to ICD 10 Mapping and Vice Versa
  • 4010 Standards to 5010 Standards (for Billing Systems)
  • DICOM Migration and many more.

 

Healthcare Data Migration: 
4 Keys to Successful Implementation


1. Develop a Long-Term Migration Strategy

  Data migration must not be treated as a quick fix. While it will certainly solve some immediate problems, healthcare companies need to make projections for at least five years when making decisions. 

2. Specify the Data for Migration

 Not every migration requires a complete relocation of all data available. Some legacy systems and data may be left in their place or moved to a different location from the other data assets in a few cases.

For this reason, taking a complete inventory of all existing data assets and deciding whether or not to move them is a necessity. When data must be transferred, the desired destination must be identified. This will prevent delays or confusion when the migration gets to a critical stage and changes become more costly and challenging to implement. 

3. Maintain Data Integrity

 Data integrity ensures that data remains accurate, consistent, and reliable while moving from one system to another. Adequate error checking and validation procedures must be in place to ensure that data is not altered or duplicated during transfer.

Most of the work required to preserve data integrity must be done during the pre-planning stage. 

4. Use a Hybrid Storage Solution

 Consider using a cloud-based storage solution to augment your on-premises storage. Many cloud service providers now offer better security, access restrictions and reliability than most in-house IT teams can provide. Cloud infrastructure enables healthcare companies to quickly acquire more storage and computing resources as needed.

While regulatory compliance requires providers to maintain in-house servers to store sensitive data, most patient health records can be kept and managed in the cloud. Using a hybrid solution that combines physical and cloud infrastructure may be the best option for large healthcare providers

References

https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0217-0

https://link.springer.com/chapter/10.1007/978-3-030-11361-2_11

https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-018-5932-5

Data sharing is key to innovation in health care

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Data sharing is key to innovation in health care

Data sharing is key to innovation in healthcare

In healthcare, sharing information is vital to the security and safety of the sector, and stakeholders within the sector.

The continued push for nationwide interoperability has helped fuel the growth of secure healthcare data sharing. Covered entities and business associates are exploring how to enhance patient care by engaging in health information exchange (HIE), but are also concerned with how they keep that data secure. Sharing patient information can help providers reduce readmissions, avoid medication errors, and even decrease duplicate testing.

What are the benefits of sharing data?

Sharing data is essential if we are to provide the very best care we can to patients. It also enables our stretched healthcare services to work in the most efficient way possible. Reducing pressure on urgent care services is a good example of the very tangible benefits of data sharing. The benefit of having access to tens of thousands of data sets at one time. Sharing information matters because we all need to be aware of what is going on and understand the consequences of what may occur.

Good information sharing is a good privacy and security practice which helps protect our organizations and our patients. Improving patient outcomes and reducing cost isn’t just about data, it’s about utilizing data effectively and turning it into a usable source.

  • Healthy patients: Monitoring applications of vital signs to ensure a proactive approach to a person’s healthy state is monumental. For example, diabetes patients can track their insulin dosages, next medical appointments, and more.
  • Cost reduction: data sharing offers the ability to manage information and use it to drive cost improvements. Healthcare organizations can identify areas where cost reductions can be made, whether related to admission rates, diagnostic tests, or operational procedures.
  • Error minimization and precise treatments: Data sharing in healthcare enables providers to deliver more accurate and personalized care treatment. By having a detailed picture of patients, it is easier to predict the response to a specific treatment.
  • Prevention services: Preventive care to provide services more efficiently, optimize operations, and improve the prevention of medical risks.
  • Streamline hospital operations: with data being generated at breakneck speed, hospitals have the demanding role of managing the operational aspects of the facility.

Learn more about data sharing in healthcare:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284141/

https://academic.oup.com/eurpub/article/29/Supplement_3/23/5628051

https://www.nap.edu/read/21703/chapter/4#37

Predictive Analytics and AI Trends Impacting Healthcare IT in 2021

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Predictive Analytics and AI Trends Impacting Healthcare IT in 2021

Predictive Analytics and AI Trends Impacting Healthcare IT

Artificial intelligence and predictive analytics continue to make transformational changes throughout the business world. While healthcare IT is seeing some impacts from AI and machine learning, it lags behind other industry sectors in fully adopting this emerging technology. However, necessity is sometimes the mother of invention, as evidenced by the Coronavirus pandemic still ravaging the planet.

Because of the strain and unpredictability COVID-19 is causing hospitals and medical establishments, faster adoption of AI is now critical. Unfortunately, healthcare IT departments suffer from a skills gap as well as other issues when implementing new systems using machine learning. Nonetheless, the demand for AI remains strong.

Here are some of the major 2021 trends related to AI in the healthcare industry:

AI Making Inroads in Healthcare over the Last Year

 The healthcare industry has seen some impacts from AI over the last year or two. For example, at-risk populations benefit from the AI-powered monitoring of chronic issues. This area especially holds promise for the treatment of COVID-19 patients; illustrating how machine learning models have the potential to augment the decision-making processes of medical professionals. Additionally, this proactive monitoring approach prevents costly emergency hospital trips.

As noted earlier, COVID-19 is driving the increased adoption of AI and ML in healthcare. One benefit is improved decision-making regarding patients, both in diagnosing the virus and in treatment. Additionally, predictive analytics helps healthcare organizations improve operational efficiency to better handle a physician shortage exacerbated by the pandemic. AI also helps doctors better diagnose diseases by using image analysis powered by machine learning algorithms. It’s another example of how ML enhances the ability of the medical professional. This image interpretation use-case applies to X-Rays, angiograms, CT, and MRI scans.

Improving Efficiency in Hospital Operations

In addition to helping hospitals streamline their operations, AI provides notable benefits in the area of revenue cycle management.  AI reduces the task of manually reviewing thousands of medical claims, and instead focuses our staff on performing warm, human outreach, and thinking through complex problems together with our members. Machine learning algorithms help hospitals perform information retrieval more effectively. These ML-powered software search agents automatically ensure patient or other data is up to date. As the use of EMR for patient data becomes more commonplace, expect AI to play a significant role in ensuring efficient data management.

The Benefits of an Open-Source AI-Layer for Databases

Data for many healthcare organizations remains difficult to access; requiring the skills of a data scientist to wean actionable information. In this scenario, an easy-to-use AI-layer able to connect to and query a database offers significant benefits. Users execute ML models with a simple SQL query. They receive relevant results along with an explanation of how the model derived its result set.

Making the power of AI/ML-driven solutions easily accessible to medical and insurance professionals improves the adoption of these nascent technologies. The ultimate result is increased efficiencies and improved benefits for the healthcare industry.

Learn more about AI trends in healthcare:

https://www.sciencedirect.com/book/9780128184387/artificial-intelligence-in-healthcare

https://www.sciencedirect.com/science/article/pii/B9780128184387000022

https://www.sciencedirect.com/science/article/pii/B978012818438700006X

 

How interoperability improves healthcare delivery and patient experience

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How interoperability improves healthcare delivery and patient experience

How Interoperability Improves Healthcare Delivery and Patient Experience

Interoperability is the ability of different information systems, devices and applications to access, exchange, integrate and cooperatively use data in a coordinated manner. 

Why is interoperability important in healthcare? 

Interoperability in healthcare leads to quality patient care, as data can flow seamlessly across the continuum of care and be accessed by relevant parties when needed.   

Benefits of healthcare interoperability  

Here are six ways interoperability improves healthcare delivery and patient experience through better information data exchange.  

Improves clinician efficiency 

With a comprehensive view of each individual patient, pulled from both traditional and non-traditional sources, health providers can make a better assessment of the patient and deliver better care.  

Enables safer transitions of care 

Patients with chronic conditions or those who are transitioning from hospital to community care require continuity of care for optimal health results. Interoperability enables safe transitions by allowing multiple health providers to access relevant patient information in a timely manner. 

For instance, a patient discharged from hospital visits the GP for an illness. The GP can see a full medical history of that patient, their recent procedures and medications prescriptions from the hospital and can then organize treatments accordingly. 

Can save time 

if a patient changes their healthcare provider, interoperability can help save time. A patient may forget details of their treatment, types of medication and relevant lab results. With interoperability, this information is easily transferred between providers and viewed by the current healthcare provider.  

Helps lower healthcare costs 

A patient who has had some lab tests recently done under their regular GP’s direction does not need to retake a blood test if they decide to visit another clinic shortly after that. The GP can view the recent test results and make clinical decisions, saving time and cost of doing more unnecessary lab tests.  

Maintains patient privacy 

Patient privacy is critical in the health sector. The right to doctor-patient privacy can benefit tremendously with interoperability. Cutting the need for clinical staff and manual updating of patient records means patient privacy can be maintained effectively. 

Reduction in errors 

Data accuracy in healthcare is crucial to avoid treatment or medication errors, as in some instance’s medication errors can occasionally pose serious health threats.  Records that are successfully exchanged across systems, typically following international standards, are guaranteed to meet data quality, thereby limiting the chance of such risks. 

Learn more about the benefits of Interoperability in healthcare:

https://ehrintelligence.com/news/3-ways-to-enhance-healthcare-interoperability-with-health-it

https://www.forbes.com/sites/forbestechcouncil/2020/05/11/how-interoperability-can-improve-patients-healthcare-process/?sh=3a82df743580