Omaha program uses HIE tech to improve postpartum care for minority parents and children

The CyncHealth health information exchange for Nebraska and Iowa and its two partners are being recognized by HHS's Racial Equity in Postpartum Care Challenge. The Department of Health and Human Services' Racial Equity in Postpartum Care Challenge has awarded CyncHealth, Collective Medical, and Innsena for their postpartum care programme in Omaha with federal cash totaling $40,000. Even among women with college degrees, pregnancy-related deaths are three to four times as prevalent among minorities than among Caucasian women. This programme significantly lowers maternal and neonatal mortality and enhances postpartum care for Black and Indigenous parents and children with high-risk illnesses who take part in Medicaid and the Children's Health Insurance Program. This initiative is the only one of the 25 winners acknowledged by HHS that makes use of health information exchange technologies to enhance individualised treatment at the local level. There are several ways to address racial equity in postpartum care:

  • Increase the diversity of healthcare providers: This can help ensure that women of color have access to culturally competent care.
  • Address structural barriers: This can include things like lack of transportation or child care, which can make it difficult for women of color to access care.
  • Address unconscious bias: This can include training healthcare providers to recognize and address their own biases in order to provide more equitable care.
  • Increase access to community-based resources: This can include things like doulas or community health workers, who can provide additional support to women of color during the postpartum period.
  • Increase funding for research on postpartum health disparities: This can help us better understand the specific challenges faced by women of color and develop targeted interventions to address them.

It's important to note that addressing racial equity in postpartum care is a complex and multifaceted issue and requires a comprehensive approach that addresses structural, systemic, and individual level factors.

The Seemingly Limitless Potential of Blockchain in Healthcare

It is important to note that there are barriers to widespread adoption of blockchain in the healthcare sector. Healthcare interoperability continues to be the Holy Grail and the ultimate objective of all companies. Given that the world's population is ageing and there is a rising lack of clinicians, there is a critical need to break down barriers and improve communication between various systems and organisations. This can help to increase efficiency, improve outcomes, and cut costs. A secure, decentralised digital ledger known as blockchain, which is most frequently connected to cryptocurrencies, is increasingly being seen as a tool to achieve interoperability or to create bridges between "data islands," or the businesses and systems where patient data may be kept (but not shared). Blockchain technology has the potential to bring several benefits to digital health innovation, including:

  • Improved data security: Blockchain technology uses a distributed ledger system, which makes it difficult for hackers to tamper with or steal data. This can help protect sensitive patient information.
  • Increased interoperability: Blockchain can help connect different systems and databases, making it easier to share and access patient information across different organizations.
  • Better patient control of their data: Blockchain-based systems can enable patients to control and share access to their own health data, giving them more control over how their information is used.
  • Enhanced clinical trial transparency: Blockchain-based systems can provide an immutable record of patient data, which can help increase transparency in clinical trial data and improve patient safety.
  • Better tracking of medical supply chain: Blockchain-based systems can also be used to track medical supply chain, to ensure the authenticity, integrity and traceability of medical products.

It's important to note that blockchain is a relatively new technology and its implementation in the healthcare industry is still in the early stage. Further research and development is needed to fully realize the potential benefits of blockchain in digital health innovation.

Understanding Machine Learning And Deep Learning In Medicine

Algorithms, datasets, machine learning, deep learning, cognitive computing, big data, and artificial intelligence: IT expressions that took over the language of 21st-century healthcare with surprising force. If medical professionals want to get ahead of the curve, they should get familiarised with the basics of AI and have an idea of what medical problems they aim to solve. So, let’s take a closer look at machine learning and deep learning in medicine.

The ante-room of artificial intelligence

The term “artificial intelligence” might be misleading as due to the overuse of the expression, its meaning started to get inflated. It implies software with cognition and sentience, a far more developed technology than how it’s used most of the time. For example, Facebook announced an AI to detect suicidal thoughts posted to its platform, but closer inspection revealed that the “AI detection” in question was little more than a pattern-matching filter that flagged posts for human community managers.

This past year has brought vast improvement in the field – at least this was when the general public learned about revolutionary new algorithms (like text-to-image DALL-E and Midjourney, and large language models like ChatGPT and Google’s MedPaLM).

However impressive these algorithms are, their cognitive capacities still stay below the average human’s. This is what we call artificial narrow intelligence (ANI), and the most advanced areas are computer vision and natural language processing.

Read on

Combating staff shortages in healthcare with digital solutions

Worldwide we are facing severe staffing shortages in healthcare, digital health solutions may be able to alleviate these shortages. Global healthcare systems are experiencing acute staff shortages, which The Guardian recently called "a ticking time bomb." According to the most recent Becker's Physician Leadership poll, the biggest problem facing healthcare organisations is a lack of qualified employees. 2 By 2030, there will be a global shortfall of 15 million healthcare workers, according to the World Health Organization (2022). 100,000 nurses, many of whom are under 35, departed the employment in the US alone between 2020 and 2021. There are numerous underlying causes of the healthcare professional shortage. The first is the demographic impact brought on by the enormous number of baby boomers who are retiring. Others are connected to medicine, such as the increase of non-communicable diseases. Digital health solutions can help combat the workforce crisis in healthcare in several ways:

  • Telemedicine: This allows healthcare providers to remotely consult with patients, reducing the need for in-person visits and freeing up time for healthcare workers.
  • Electronic health records (EHRs): EHRs can help streamline the process of collecting and sharing patient information, reducing the need for additional staff to manage paper records.
  • Automated appointment scheduling: Digital health solutions can automate the process of scheduling appointments, reducing the need for administrative staff.
  • Virtual care: Virtual care enables patients to interact with healthcare providers remotely, via video, audio, or text, reducing the need for in-person visits and freeing up time for healthcare workers.
  • AI-powered triage: AI-powered triage can assist healthcare workers in identifying patients with urgent needs, so they can be seen more quickly.
  • Wearable technology: Wearable technology can help monitor patients remotely, reducing the need for in-person visits and freeing up time for healthcare workers.
  • Remote patient monitoring: With remote patient monitoring, patients can be monitored remotely, reducing the need for in-person visits and freeing up time for healthcare workers.

Overall, digital health solutions can help healthcare providers better allocate their resources and reduce the need for additional staff.

Is AI-Assisted Lung Cancer Diagnosis Right For Your Hospital?

The potent combination of NLP and AI-assisted diagnostic tools for early-stage lung cancers represents a solution for healthcare systems. Around 1.8 million people die from lung cancer each year, making it the most common type of cancer to cause mortality worldwide. The majority of patients receive a diagnosis once symptoms have developed and the disease has reached an advanced stage (Stage III or IV), which accounts for the 20 percent five-year survival rate that is currently the norm worldwide. Small lung tumours, on the other hand, have a survival rate as high as 90% when they are treated at Stage 1A. This large variation emphasises the urgent necessity for lung cancer to be identified and treated at the earliest feasible stage. The two million Americans who are diagnosed with lung cancer each year present one of the best opportunities to detect more tiny, pre-symptomatic lung malignancies early. Before implementing AI-assisted technologies for lung cancer diagnosis in your hospital, it is important to consider the following:

  • Validation and accuracy: Make sure that the AI technology you are considering has been validated and found to have a high level of accuracy in detecting lung cancer. This includes comparing the AI's performance to that of experienced radiologists.
  • Data privacy and security: Ensure that the AI technology you are using is compliant with all data privacy and security regulations, and that appropriate measures are in place to protect patient information.
  • Integration with existing systems: Consider how the AI technology will integrate with your hospital's existing systems, such as electronic health records (EHRs) and picture archiving and communication systems (PACS).
  • Training and support: Ensure that your staff will be properly trained on how to use the AI technology, and that support will be available if any issues arise.
  • Human oversight: It is crucial to have human oversight for AI-assisted diagnostics, as the technology should be used as a tool to support radiologists' decision-making and not replace them.
  • Explainable AI: Have a clear understanding of the AI algorithms, its internal workings, the data used for training and its limitations.
  • Clinical workflow: Evaluate how the technology will fit into the existing clinical workflow and whether it will improve the patient experience and the overall diagnostic process.
  • Cost-benefit analysis: Consider the costs and benefits of implementing the AI technology, including any potential cost savings or revenue increases, as well as any additional costs associated with training, support, and maintenance.

Overall, it is important to thoroughly evaluate the AI technology and its potential impact on patient care and the hospital's operations before implementing it in your hospital.

2023 Strategic Workforce Challenges

Staff shortages, long hours with no breaks, delays in receiving necessary supplies, and overall low morale have brought forth new core challenges that we must continue to address nationwide as we enter 2023. For society as a whole, the 2020s have been a decade of difficulties and adversity. As it was at the forefront of many of these studies, healthcare has taken the brunt of these difficulties. Over the previous three years, starting at the beginning of the Pandemic, healthcare personnel have overcome many of the challenges that were placed in their path. As we move towards 2023, we must continue to address additional national core concerns caused by staff shortages, long hours without breaks, delays in acquiring required supplies, and general low morale. We must recruit and keep competent healthcare professionals. With ongoing pressure to address personnel gaps, health systems have had to deal with extremely high demand. There are several tools that can be used to resolve the healthcare workforce crisis, including:

Recruitment and retention strategies: Organizations can implement strategies to attract and retain healthcare workers, such as offering competitive salaries and benefits, providing training and development opportunities, and creating a positive work-life balance.

  • Telehealth and virtual care: Telehealth and virtual care can help to increase access to healthcare services, by allowing patients to receive care remotely. This can help to ease the burden on the healthcare workforce by reducing the need for in-person visits.
  • Task shifting: Task shifting involves training non-clinical staff to perform certain tasks, such as administering medication or taking vital signs, which can help to increase the overall capacity of the healthcare workforce.
  • Automation and technology: Automation and technology can help to streamline certain tasks and processes, such as scheduling appointments or managing patient records, which can help to free up the time of healthcare workers.
  • Community-based healthcare: Community-based healthcare can involve partnering with non-traditional healthcare providers, such as community health workers or faith-based organizations, to provide care to underserved communities.
  • Public-private partnership: Governments and private sectors can partner to provide more healthcare services, training and education opportunities, to increase the number of healthcare providers.
  • International recruitment: Organizations can also recruit healthcare professionals from other countries to help meet the demand for healthcare services.

These tools can be used in combination to help resolve the healthcare workforce crisis, by increasing the overall capacity of the healthcare workforce, and improving access to care for patients.

EHRA questions rationale of added TEFCA security protocols

While acknowledging that securing access to data is a shared goal, the HIMSS Electronic Health Record Association suggests ONC's interoperability framework does not consider existing controls, or certain business standards and workflows. The EHR Association suggests that workforce authentication requirements only be applied to the Qualified Health Information Network workforce, with special consideration given to participants and sub-participants who are not HIPAA-covered entities, in its comments to the ONC on the draught QHIN, Participant and Subparticipant Additional Requirements SOP. The Trusted Exchange Framework and Common Agreement, which were created by the enlisted Sequoia project, have been proposed requirements for QHINs, participants, and sub-participants. The Office of the National Coordinator for Health Information Technology (ONC) is accepting comments on these proposals. The EHRA advised in its letter dated January 13 that the breadth of the auditing standards and requirements for worker authentication be reduced and that it is uncertain if further requirements are necessary or beneficial. TEFCA (Trusted Exchange Framework and Common Agreement) security protocols are important in patient record access because they establish a standardized set of security measures that must be followed by all participating organizations in order to ensure the confidentiality, integrity, and availability of patient data. This includes measures such as authentication, access control, and encryption to protect patient data from unauthorized access or disclosure. Additionally, TEFCA sets guidelines for incident management and reporting, which helps to ensure that any security breaches are identified and addressed quickly. Overall, the implementation of TEFCA security protocols helps to promote trust and confidence in the electronic exchange of patient data, which is essential for effective and efficient care coordination across healthcare organizations.

Google Research and DeepMind develop AI medical chatbot

A new AI-powered medical-specific chatbot developed by Google and DeepMind has shown some potential for clinical applications. Using datasets from professional medical exams, research, and consumer questions, Google Research and DeepMind have constructed a substantial language model for the medical community. The AI-powered chatbot MedPaLM combines six existing open-question answering datasets with HealthSearchQA, a free-response collection of online medical questions created by Google and DeepMind. The additional six datasets were gathered from MedQA, MedMCQA, PubMedQA, LiveQA, MedicationQA, and MMLU. Both medical experts and laypeople who are not professionals in the field of medicine may submit multiple-choice questions. Large language models (LLMs), like MedPaLM, are made to comprehend questions and produce suitable plain-language answers. They use data from huge datasets for this. MultiMedQA, an open-source benchmark for medical question-answering, is used to measure the performance of the technology. AI chatbots can be used in a variety of ways to improve patient experience, including:

  • Virtual triage and symptom checking: Chatbots can assist patients in determining the severity of their symptoms and whether they need to seek medical attention.
  • Appointment scheduling: Chatbots can help patients schedule appointments and provide reminders about upcoming appointments.
  • Medication management: Chatbots can assist patients in managing their medication regimen, including providing information about dosage and potential side effects.
  • Health education: Chatbots can provide patients with educational resources and information about their health conditions.
  • Post-discharge care: Chatbots can help to support patients following their discharge from a hospital or other medical facility, by answering questions and providing guidance on recovery.
  • Language Translation: Chatbots can be used to provide language support for non-English speaking patients, helping them to communicate with medical staff.
  • 24/7 availability: Chatbots can provide patients with access to medical information and support 24/7, regardless of the availability of healthcare providers.

Overall, AI chatbots can help to improve patient experience by providing convenient and accessible healthcare services, reducing wait times, and increasing patient engagement and education.

AI, chatbots, data monetization: Most disruptive health tech trends of '23

According to a forecast by professional services company KPMG, health IT acquisitions are anticipated to be at prepandemic levels in 2023, with an uptick in activity for middle-market enterprises in the $200 million to $1 billion range.

From the 2023 KPMG Healthcare and Life Sciences Investment Outlook, which was released on Jan. 9 and questioned 311 corporate and private equity deal-makers, the following six more health IT themes are presented:

1. According to the majority of survey participants, the need for health IT will be driven by the transition to value-based care and continued waste-reduction initiatives.

2. New wearable or monitoring gadgets, data mapping for clinical trials, and Oracle Cerner's envisioned health management software are a few further trends that are anticipated for 2023.

3. A large number of health IT acquisitions in 2023 are anticipated to aid payers and providers in enhancing productivity, clinical trial outcomes, and clinical care.

4. Artificial intelligence and machine learning, natural language processing, HIPPA-compliant voice and chatbot applications, telemedicine, remote patient monitoring, data interoperability, cloud computing, chronic disease management, data monetization, and blockchain will be the most revolutionary technological trends in 2023.

5. Increased innovation, payers and providers looking for goods and services that help save costs, and a turbulent stock market making mergers and acquisitions more alluring than public offerings are some positive factors for health IT.

6. The market for telehealth and several other consumer-facing technology is saturated, and there are significant budgetary constraints that may prevent some companies from investing in health IT.

Hospitals rank low across industries for patient and employee satisfaction

Experiences for both groups are often lacking, according to a new report from Qualtrics, which suggests healthcare leaders act quickly to put empathy into action with "meaningful digital transformation." The goal of the 2023 Healthcare Experience Trends report is to give healthcare leaders a global overview of insights and suggestions on what clients should anticipate from them and what staff members require. Qualtrics, a provider of an HITRUST-certified and FEDRAMP-compliant experience management platform, conducted surveys of over 9,000 customers in 29 countries and 3,000 healthcare professionals in 27 countries for the analysis. According to the researchers, "they desire respect, convenience, and human connection." While patient satisfaction is 3% lower (74%) and the likelihood that patients will suggest providers is 2% lower (70%), respectively, than the cross-industry global average, patient trust in providers is 5% greater at 79%. Patient trust in providers grew by 1% as compared to Qualtrics 2022 trends data, while their propensity to recommend providers decreased by 1%. Hospitals can rank low in patient experience for a number of reasons. Some of the common factors that contribute to low patient satisfaction scores in hospitals include:

  • Long wait times: Patients may become frustrated if they have to wait a long time to be seen by a healthcare provider or to receive treatment. This can be due to a shortage of staff or an overbooked schedule.
  • Lack of communication: Poor communication between healthcare providers and patients can lead to confusion and dissatisfaction. Patients may feel that their questions are not being answered or that they are not being kept informed about their care.
  • Poor quality of care: Patients may feel that the care they received was not of a high quality or that their needs were not met. This can be due to a lack of training or experience among healthcare staff, or a lack of resources or equipment.
  • Inadequate pain management: Patients may feel that their pain was not adequately managed during their hospital stay, leading to dissatisfaction.
  • Limited access to amenities: Patients may be dissatisfied if they do not have access to amenities such as private rooms, television, or internet access.
  • Inadequate patient education: Patients may be dissatisfied if they do not receive adequate education about their condition, treatment options, or discharge instructions.
  • Inadequate emotional support: Patients may be dissatisfied if they do not receive emotional support or understanding during their hospital stay.
  • Privacy and security concerns: Patients may be dissatisfied if they feel that their privacy and security have not been adequately protected.

Hospitals are working hard to improve the patient experience by addressing these and other issues through various strategies such as the use of patient satisfaction surveys, the implementation of patient-centered care models, and the use of technology to improve communication and efficiency.

Top Artificial Intelligence Companies In Healthcare To Keep An Eye On

Read about the biggest artificial intelligence companies in healthcare ranging from start-ups to tech giants to keep an eye on in the future. Medical AI is a booming field. More and more businesses are aiming to use artificial intelligence to revolutionise the healthcare industry. It might be challenging to stay current with the most promising firms given how quickly these businesses come and go. I've gathered the top brands, from start-ups to tech behemoths, here for you to keep an eye on in the future. The Medical Futurist team created an e-book that is simple to read about that topic to further assist you in staying current with what AI will bring to medicine. You should definitely read it, and I'd love to know what you think. Artificial intelligence must rethink healthcare and will do so. Nobody contests the incredible promise of artificial intelligence. Artificial intelligence (AI) has the potential to revolutionize healthcare in a number of ways. Here are a few examples of how AI can be used to redesign healthcare:

  • Diagnosis and treatment: AI algorithms can be trained to analyze medical images, such as X-rays and CT scans, to help identify and diagnose diseases. AI can also be used to recommend treatment options based on a patient's medical history, symptoms, and other factors.
  • Predictive analytics: AI can be used to analyze large amounts of patient data, such as electronic health records (EHRs), to predict patient outcomes and identify potential health risks. This can help healthcare providers to make more informed decisions about patient care.
  • Clinical decision support: AI can be used to provide real-time, evidence-based recommendations to healthcare providers at the point of care, helping to improve the quality of care and reduce errors.
  • Personalized medicine: AI can be used to analyze a patient's genetic information and medical history to identify personalized treatment options that are most likely to be effective.
  • Patient engagement: AI-powered chatbots and virtual assistants can be used to provide patients with access to health information and support, helping to improve patient engagement and self-management.
  • Automation: AI can be used to automate repetitive tasks, such as data entry and claims processing, freeing up healthcare staff to focus on more important tasks.
  • Quality control: AI can be used to analyze large amounts of data, such as clinical trial results, to identify patterns and trends that can be used to improve the quality of care.
  • Research and drug discovery: AI can be used to analyze large amounts of data, such as genomic data, to identify new drug targets and accelerate the drug discovery process.

While AI has the potential to greatly improve healthcare, it is important to note that AI should be integrated with human intelligence and expertise to ensure that it is used ethically and effectively, and to help mitigate potential unintended consequences.