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AI Transforming Precision Medicine

How AI-Driven Data Curation is Transforming Precision Medicine Precision medicine—a healthcare approach that personalizes disease prevention and treatment based on insights into a patient’s genes, environment, and behavior—holds incredible promise. However, its success depends on high-quality, curated data from sources like electronic health records (EHRs). This reliance creates significant challenges for healthcare providers and researchers. Can artificial intelligence (AI) help address these hurdles? AI-enabled data curation is already making strides in advancing precision medicine, particularly in oncology. By analyzing vast datasets, including structured and unstructured information, AI is helping healthcare organizations accelerate research and improve patient outcomes. Data Curation Challenges in Precision Medicine Real-world data (RWD) is a key driver of precision medicine, but processing this data is fraught with challenges. According to Dr. C.K. Wang, Chief Medical Officer at COTA, Inc., EHRs provide unprecedented access to detailed patient information, enabling deeper insights into care patterns. However, much of this data resides in unstructured formats, such as clinicians’ notes, making it difficult to extract and analyze. “To transform this unstructured data into actionable insights, significant human expertise and resources are required,” Wang explained. While AI tools like COTA’s CAILIN, which uses advanced search capabilities, streamline this process, human involvement remains essential. Wang emphasized that even with the rapid advancements in AI, healthcare data curation requires expert oversight to ensure quality and reliability. “The adage ‘junk in, junk out’ applies here—without high-quality training data, AI cannot generate meaningful insights,” he noted. PHI and COTA: A Collaborative Approach to AI-Driven Curation To overcome these challenges, Precision Health Informatics (PHI), a subsidiary of Texas Oncology, partnered with COTA to enhance their data curation capabilities. The collaboration aims to integrate structured and unstructured data, including clinician notes and patient-reported outcomes, into a unified resource for precision medicine. PHI’s database, which represents 1.6 million patient journeys, provides a rich resource for hypothesis-driven studies and clinical trial enrichment. However, much of this data was siloed or unstructured, requiring advanced tools and expert intervention. Lori Brisbin, Chief Operating Officer at PHI, highlighted the importance of partnering with a data analytics leader. “COTA’s strong clinical knowledge in oncology allowed them to identify data gaps and recommend improvements,” she said. This partnership is yielding significant results, including a high data attrition rate of 87%—far surpassing the industry average of 50% for similar projects. The Role of AI in Cancer Care AI tools like CAILIN are helping PHI and COTA refine data curation processes by: Brisbin likened the role of AI to sorting images: “If you’re looking for German shepherds, AI will narrow the search but might include similar images, like wolves or huskies. Experts are still needed to validate and refine the results.” Building the Foundation for Better Outcomes The integration of high-quality RWD into analytics efforts is reshaping precision medicine. While clinical trial data offers valuable insights, it often lacks the variability seen in real-world scenarios. Adding RWD to these datasets helps expand the scope of research and ensure broader applicability. For instance, cancer care guidelines developed with RWD can account for diverse patient populations and treatment approaches. COTA’s work with PHI underscores the value of collaborative data curation, with AI streamlining processes and human experts ensuring accuracy. The Future of AI in Precision Medicine As healthcare organizations invest in data-driven innovation, AI will play an increasingly pivotal role in enabling precision medicine. However, challenges remain. Wang noted that gaps in EHR data, such as missing survival metrics, can undermine oncological outcomes research. Advances in interoperability and external data sources will be key to addressing these issues. “The foundation of our partnership is built on leveraging data insights to enhance care quality and improve operational efficiency,” Wang said. Through AI-powered tools and meaningful partnerships, precision medicine is poised to deliver transformative results, empowering providers to offer tailored treatments that improve patient outcomes at scale. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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AI-Driven Care Coordination Software

AI-Driven Care Coordination Software

Can AI-Driven Care Coordination Software Improve Workflows? University Hospitals is leveraging AI to enhance care coordination across its network of 13 hospitals and numerous outpatient settings. This effort highlights the transformative potential of AI-driven platforms in streamlining workflows, improving patient outcomes, and addressing clinician burnout. The Role of AI in Care Coordination Care coordination ensures seamless collaboration between healthcare providers, aiming for safe, appropriate, and effective treatment. Effective information-sharing can: According to the U.S. Centers for Medicare & Medicaid Services (CMS), poor care coordination can lead to: The Agency for Healthcare Research and Quality (AHRQ) advocates for a mix of technology adoption and care-specific strategies, such as proactive care plans tailored to patient needs. While electronic health records (EHRs) aid in these efforts, AI’s ability to analyze vast data sets positions it as the next evolution in care coordination. University Hospitals’ AI Initiative University Hospitals has partnered with Aidoc to deploy its AI-powered platform, aiOS, to improve radiology and care coordination workflows. Chair of Radiology Donna Plecha shared insights on how AI is already assisting in their operations: Best Practices for Implementing AI 1. Identify High-Value Use Cases: 2. Conduct Architectural Reviews: 3. Monitor ROI and Metrics: 4. Gain Clinician Buy-In: Looking Ahead AI is proving to be a valuable tool in care coordination, but its adoption requires realistic expectations and a thoughtful approach. Plecha underscores that AI won’t replace radiologists but will empower those who embrace it. As healthcare faces increasing patient volumes and clinician shortages, leveraging AI to reduce workloads and enhance care quality is becoming a necessity. With ongoing evaluations and phased implementations, University Hospitals is setting a precedent for how AI can drive innovation in care coordination while maintaining clinician oversight and patient trust. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI-Driven Healthcare Approvals

AI-Driven Healthcare Approvals

Salesforce and Blue Shield of California are launching an AI-driven system to streamline healthcare approvals, aiming to cut down prior authorization wait times from weeks to, potentially, the same day. This partnership, leveraging Salesforce’s healthcare cloud, integrates patient data to streamline approvals while retaining clinician oversight, ensuring AI decisions are always reviewed by a human expert.

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Data Analytics for Disease Management

Data Analytics for Disease Management

Healthcare IT advancements, especially electronic health records (EHRs), have made it easier to gather and store data, which, in turn, fuels population health initiatives and improves patient outcomes. The Agency for Healthcare Research and Quality highlights that using health IT tools can significantly enhance chronic disease management by promoting efficient care delivery, information-sharing, and patient education. However, selecting and adopting the right analytics tools remains challenging. Here are five essential data analytics tools that healthcare providers can leverage for effective chronic disease management.

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UX Principles for AI in Healthcare

UX Principles for AI in Healthcare

The Role of UX in AI-Driven Healthcare AI is poised to revolutionize the global economy, with predictions it could contribute $15.7 trillion by 2030—more than the combined economic output of China and India. Among the industries likely to see the most transformative impact is healthcare. However, during my time at NHS Digital, I saw how systems that weren’t designed with existing clinical workflows in mind added unnecessary complexity for clinicians, often leading to manual workarounds and errors due to fragmented data entry across systems. The risk is that AI, if not designed with user experience (UX) at the forefront, could exacerbate these issues, creating more disruption rather than solving problems. From diagnostic tools to consumer health apps, the role of UX in AI-driven healthcare is critical to making these innovations effective and user-friendly. This article explores the intersection of UX and AI in healthcare, outlining key UX principles to design better AI-driven experiences and highlighting trends shaping the future of healthcare. The Shift in Human-Computer Interaction with AI AI fundamentally changes how humans interact with computers. Traditionally, users took command by entering inputs—clicking, typing, and adjusting settings until the desired outcome was achieved. The computer followed instructions, while the user remained in control of each step. With AI, this dynamic shifts dramatically. Now, users specify their goal, and the AI determines how to achieve it. For example, rather than manually creating an illustration, users might instruct AI to “design a graphic for AI-driven healthcare with simple shapes and bold colors.” While this saves time, it introduces challenges around ensuring the results meet user expectations, especially when the process behind AI decisions is opaque. The Importance of UX in AI for Healthcare A significant challenge in healthcare AI is the “black box” nature of the systems. For example, consider a radiologist reviewing a lung X-ray that an AI flagged as normal, despite the presence of concerning lesions. Research has shown that commercial AI systems can perform worse than radiologists when multiple health issues are present. When AI decisions are unclear, clinicians may question the system’s reliability, especially if they cannot understand the rationale behind an AI’s recommendation. This opacity hinders feedback, making it difficult to improve the system’s performance. Addressing this issue is essential for UX designers. Bias in AI is another significant issue. Many healthcare AI tools have been documented as biased, such as systems trained on predominantly male cardiovascular data, which can fail to detect heart disease in women. AIs also struggle to identify conditions like melanoma in people with darker skin tones due to insufficient diversity in training datasets. UX can help mitigate these biases by designing interfaces that clearly explain the data used in decisions, highlight missing information, and provide confidence levels for predictions. The movement toward eXplainable AI (XAI) seeks to make AI systems more transparent and interpretable for human users. UX Principles for AI in Healthcare To ensure AI is beneficial in real-world healthcare settings, UX designers must prioritize certain principles. Below are key UX design principles for AI-enabled healthcare applications: Applications of AI in Healthcare AI is already making a significant impact in various healthcare applications, including: Real-world deployments of AI in healthcare have demonstrated that while AI can be useful, its effectiveness depends heavily on usability and UX design. By adhering to the principles of transparency, interpretability, controllability, and human-centered AI, designers can help create AI-enabled healthcare applications that are both powerful and user-friendly. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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Ambient AI Enhances Patient-Provider Relationship

Ambient AI Enhances Patient-Provider Relationship

How Ambient AI is Enhancing the Patient-Provider Relationship Ambient AI is transforming the patient-provider experience at Ochsner Health by enabling clinicians to focus more on their patients and less on their screens. While some view technology as a barrier to human interaction, Ochsner’s innovation officer, Dr. Jason Hill, believes ambient AI is doing the opposite by fostering stronger connections between patients and providers. Researchers estimate that physicians spend over 40% of consultation time focused on electronic health records (EHRs), limiting face-to-face interactions. “We have highly skilled professionals spending time inputting data instead of caring for patients, and as a result, patients feel disconnected due to the screen barrier,” Hill said. Additionally, increased documentation demands related to quality reporting, patient satisfaction, and reimbursement are straining providers. Ambient AI scribes help relieve this burden by automating clinical documentation, allowing providers to focus on their patients. Using machine learning, these AI tools generate clinical notes in seconds from recorded conversations. Clinicians then review and edit the drafts before finalizing the record. Ochsner began exploring ambient AI several years ago, but only with the advent of advanced language models like OpenAI’s GPT did the technology become scalable and cost-effective for large health systems. “Once the technology became affordable for large-scale deployment, we were immediately interested,” Hill explained. Selecting the Right Vendor Ochsner piloted two ambient AI tools before choosing DeepScribe for an enterprise-wide partnership. After the initial rollout to 60 physicians, the tool achieved a 75% adoption rate and improved patient satisfaction scores by 6%. What set DeepScribe apart were its customization features. “We can create templates for different specialties, but individual doctors retain control over their note outputs based on specific clinical encounters,” Hill said. This flexibility was crucial in gaining physician buy-in. Ochsner also valued DeepScribe’s strong vendor support, which included tailored training modules and direct assistance to clinicians. One example of this support was the development of a software module that allowed Ochsner’s providers to see EHR reminders within the ambient AI app. “DeepScribe built a bridge to bring EHR data into the app, so clinicians could access important information right before the visit,” Hill noted. Ensuring Documentation Quality Ochsner has implemented several safeguards to maintain the accuracy of AI-generated clinical documentation. Providers undergo training before using the ambient AI system, with a focus on reviewing and finalizing all AI-generated notes. Notes created by the AI remain in a “pended” state until the provider signs off. Ochsner also tracks how much text is generated by the AI versus added by the provider, using this as a marker for the level of editing required. Following the successful pilot, Ochsner plans to expand ambient AI to 600 clinicians by the end of the year, with the eventual goal of providing access to all 4,700 physicians. While Hill anticipates widespread adoption, he acknowledges that the technology may not be suitable for all providers. “Some clinicians have different documentation needs, but for the vast majority, this will likely become the standard way we document at Ochsner within a year,” he said. Conclusion By integrating ambient AI, Ochsner Health is not only improving operational efficiency but also strengthening the human connection between patients and providers. As the technology becomes more widespread, it holds the potential to reshape how clinical documentation is handled, freeing up time for more meaningful patient interactions. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Gen AI to Predict and Automate Discharge

Gen AI to Predict and Automate Discharge

While electronic health records (EHRs) have improved data exchange for care coordination, they have also increased the clinical documentation burden on healthcare providers. Research from 2023 suggests that clinicians may now spend more time on EHRs than on direct patient care. On average, providers spend over 36 minutes on EHR tasks for every 30-minute patient visit. Generative AI, however, holds the potential to transform this. As defined by the Government Accountability Office, generative AI (GenAI) is a technology that can create content—such as text, images, audio, or video—based on user prompts. With the rise of chatbot interfaces like Chat-GPT, health IT vendors and healthcare systems are piloting GenAI tools to streamline clinical documentation. While the technology shows promise in reducing the documentation burden and mitigating clinician burnout, several challenges still hinder widespread adoption. Ambient Clinical Intelligence Ambient clinical intelligence leverages smartphone microphones and GenAI to transcribe patient encounters in real time, producing draft clinical documentation for providers to review within seconds. A 2024 study examined the use of ambient AI scribes by 10,000 physicians and staff at The Permanente Medical Group. The results were promising—providers reported better patient conversations and less after-hours EHR documentation. However, accuracy is critical for patient safety. A 2023 study found that ambient AI tools struggle with non-lexical conversational sounds (NLCSes)—like “mm-hm” and “uh-uh”—which patients and providers use to convey information. For instance, a patient might say “Mm-hm” to confirm they have no allergies to antibiotics. The study found that while the AI tools had a word error rate of 12% for all words, the error rate for NLCSes conveying clinically relevant information was as high as 98.7%. These inaccuracies could lead to patient safety risks, highlighting the importance of provider review. Patient Communication Patient portal messaging has surged since the COVID-19 pandemic, with a 2023 report showing a 157% increase in messages compared to pre-pandemic levels. To manage inbox overload, healthcare systems are exploring generative AI for drafting responses to patient messages. Clinicians review and edit these drafts before sending them to patients. A 2024 study found that primary care physicians rated AI-generated responses higher in communication style and empathy than those written by providers. However, the AI-generated responses were often longer and more complex, posing challenges for patients with lower health or English literacy. There are also potential risks to clinical decision-making. A 2024 simulation study revealed that the content of replies to patient messages changed when physicians used AI assistance, introducing an automation bias that could impact patient outcomes. Although most AI-generated drafts posed minimal safety risks, a small portion, if left unedited, could result in severe harm or death. Although GenAI may reduce the cognitive load of writing replies, it might not significantly decrease the overall time spent on patient communications. A recent study showed that while providers felt less emotional exhaustion when using AI to draft messages, the time spent on replying, reading, and writing messages remained unchanged from pre-pilot levels. Discharge Summaries Generative AI has also been shown to improve the readability of patient discharge summaries. A study published in JAMA Network Open demonstrated that GenAI could lower the reading level of discharge notes from an eleventh-grade to a sixth-grade level, which is more appropriate for diverse health literacy levels. However, accuracy is still a concern. Physician reviews of these AI-generated summaries found that while some were complete, others contained omissions and inaccuracies that raised safety concerns. Balancing AI’s Benefits with Oversight While generative AI shows promise in alleviating the documentation burden and improving patient communication, challenges remain. Issues such as accurately capturing non-verbal cues and ensuring document accuracy underscore the need for careful provider oversight. As AI technologies continue to evolve, ensuring that the benefits are balanced with provider review will be crucial for safe and effective healthcare implementation. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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Discharge Planning

Discharge Planning

Discharge planning is crucial for smoothly transitioning patients from hospital care to the next stage of their recovery. This process requires collaboration among patients, caregivers, and providers to create a personalized plan that ensures continuity of care after hospitalization. Effective discharge planning must consider the patient’s care needs, preferences, and concerns. When done well, it helps prevent readmissions and alleviates strain on both patients and hospitals. However, balancing clinical judgment with patient data can challenge care teams already burdened with heavy workloads. Jean Halpin, COO at Grant Medical Center, shared how the organization has integrated AI tools to predict discharge dates and automate parts of the discharge planning process, helping to streamline patient care. Challenges of Effective Discharge Planning Halpin emphasized that a streamlined discharge process is essential for reducing wait times and improving patient engagement. Yet, various factors influence how quickly patients are discharged, particularly in emergency rooms where delays can affect overall patient flow. “Most of the wait time we experience as patients boils down to a lengthy discharge process that isn’t effectively moving patients,” Halpin explained. “It’s a domino effect. Someone waiting in the ER for a bed is delayed because another patient hasn’t been discharged when they should have been.” To address these inefficiencies, Grant Medical Center implemented the Qventus Inpatient Solution. This tool integrates with electronic health records (EHRs) to analyze patient data—such as clinical notes, history, and labs—and provides recommendations on discharge timing. These insights have helped reduce ER wait times and improved patient flow. Integrating AI into Clinical Workflows Adopting AI in healthcare comes with integration challenges, particularly ensuring that tools enhance, rather than hinder, clinicians’ workflows. Halpin noted that the Qventus tool minimizes disruptions by seamlessly pulling EHR data to generate an estimated discharge date, allowing care teams to focus on patient care without extra administrative burdens. “As a patient’s health changes, the [discharge] date can fluctuate, but AI uses its data to predict the most accurate day based on similar cases,” Halpin explained. “The care teams can then review the date and determine whether they agree, without having to sift through records to develop their own recommendation.” Halpin also highlighted the value of AI in reducing the administrative load. Tasks like coordinating discharges to rehab facilities, ordering tests, and prescribing medication consume significant time, and automating these functions allows care teams to focus more on direct patient care. Embracing AI to Alleviate Healthcare Worker Burdens For healthcare systems adopting AI, accurately assessing its impact is critical. At Grant Medical Center, leadership is measuring success by evaluating employee satisfaction, patient outcomes, and administrative improvements—such as time and cost savings. “By improving our patient flow, we reduced unnecessary stays by nearly 1,400 days. Patients are happy to go home on time, and our care teams can focus on working at the top of their license,” said Halpin. Despite the benefits, Halpin stressed that implementing AI requires thoughtful onboarding to ensure staff are comfortable with the new tools. Training and support are key to making the transition seamless and enabling teams to see how AI can enhance their workflows. “Health system leaders should embrace advancements that help alleviate burdens for workers,” she said. “Once teams understand the tool, they can prioritize patient care while AI handles the time-consuming admin tasks.” Halpin concluded that embracing AI in discharge planning not only improves operational efficiency but also empowers healthcare teams to deliver better, more focused care. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Transformative Potential of AI in Healthcare

Transformative Potential of AI in Healthcare

Healthcare leaders are increasingly optimistic about the transformative potential of AI and data analytics in the industry, according to a new market research report by Arcadia and The Harris Poll. The report, titled “The Healthcare CIO’s Role in the Age of AI,” reveals that 96% of healthcare executives believe AI adoption can provide a competitive edge, both now and in the future. While one-third of respondents see AI as essential today, 73% believe it will become critical within the next five years. How AI is Being Used in Healthcare The survey found that 63% of healthcare organizations are using AI to analyze large patient data sets, identifying trends and informing population health management. Additionally, 58% use AI to examine individual patient data to uncover opportunities for improving health outcomes. Nearly half of the respondents also reported using AI to optimize the management of electronic health records (EHRs). These findings align with a similar survey conducted by the University of Pittsburgh Medical Center’s Center for Connected Medicine (CCM), which highlighted AI as the most promising emerging technology in healthcare. The focus on AI stems from its ability to break down data silos and make use of the vast amount of clinical data healthcare organizations collect. “Healthcare leaders are preparing to harness AI’s full potential to reform care delivery,” said Aneesh Chopra, Arcadia’s chief strategy officer. “With secure data sharing scaling across the industry, technology leaders are focusing on platforms that can organize fragmented patient records into actionable insights throughout the patient journey.” Supporting Strategic Priorities with AI AI and data analytics are also seen as critical for maintaining competitiveness and resilience, particularly as organizations face digital transformation and financial challenges. In fact, 83% of respondents indicated that data-driven tools could help them stay ahead in these areas. Technology-related priorities, such as adopting an enterprise-wide approach to data analytics (44%) and enhancing decision-making through AI (41%), were top of mind for many healthcare leaders. Improving patient experience (40%), health outcomes (35%), and patient engagement (29%) were also highlighted as key strategic goals that AI could help achieve. Challenges in AI Adoption While most healthcare leaders are confident about adopting AI (96%), they also feel pressure to do so quickly, with the push primarily coming from data and analytics teams (82%), IT teams (78%), and executives (73%). One major obstacle is the lack of talent. Approximately 40% of respondents identified the shortage of skilled professionals as a top barrier to AI adoption. To address this, organizations are seeing increased demand for skills related to data analysis, machine learning, and systems integration. Additionally, 71% of IT leaders emphasized the growing need for data-driven decision-making skills. The Evolving Role of CIOs The rise of AI is reshaping the role of CIOs in healthcare. Nearly 87% of survey respondents see themselves as strategic influencers in setting and refining AI-related strategies, rather than just implementers. However, many CIOs feel constrained by the demands of day-to-day operations, with 58% reporting that tactical execution takes precedence over long-term AI strategy development. Leaders agree that to be effective, CIOs and their teams should focus more on strategic planning, dedicating around 75% of their time to developing and implementing AI strategies. Communication and workforce readiness are also crucial, with 75% of respondents citing poor communication between IT teams and clinical staff as a barrier to AI success, and 40% noting that clinical staff need more support to utilize data analytics effectively. “CIOs and their teams are setting the stage for an AI-driven transformation in healthcare,” said Michael Meucci, president and CEO of Arcadia. “The findings show that a robust data foundation and an evolving workforce are key to realizing AI’s full potential in patient care and healthcare operations.” Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI and Big Data

AI and Big Data

Over the past decade, enterprises have accumulated vast amounts of data, capturing everything from business processes to inventory statistics. This surge in data marked the onset of the big data revolution. However, merely storing and managing big data is no longer sufficient to extract its full value. As organizations become adept at handling big data, forward-thinking companies are now leveraging advanced analytics and the latest AI and machine learning techniques to unlock even greater insights. These technologies can identify patterns and provide cognitive capabilities across vast datasets, enabling organizations to elevate their data analytics to new levels. Additionally, the adoption of generative AI systems is on the rise, offering more conversational approaches to data analysis and enhancement. This allows organizations to extract significant insights from information that would otherwise remain untapped in data stores. How Are AI and Big Data Related? Applying machine learning algorithms to big data is a logical progression for companies aiming to maximize the potential of their data. Unlike traditional rules-based approaches that follow explicit instructions, machine learning systems use data-driven algorithms and statistical models to analyze and detect patterns in data. Big data serves as the raw material for these systems, which derive valuable insights from it. Organizations are increasingly recognizing the benefits of integrating big data with machine learning. However, to fully harness the power of both, it’s crucial to understand their individual capabilities. Understanding Big Data Big data involves extracting and analyzing information from large quantities of data, but volume is just one aspect. Other critical “Vs” of big data that enterprises must manage include velocity, variety, veracity, validity, visualization, and value. Understanding Machine Learning Machine learning, the backbone of modern AI, adds significant value to big data applications by deriving deeper insights. These systems learn and adapt over time without the need for explicit programming, using statistical models to analyze and infer patterns from data. Historically, companies relied on complex, rules-based systems for reporting, which often proved inflexible and unable to cope with constant changes. Today, machine learning and deep learning enable systems to learn from big data, enhancing decision-making, business intelligence, and predictive analysis. The strength of machine learning lies in its ability to discover patterns in data. The more data available, the more these algorithms can identify patterns and apply them to future data. Applications range from recommendation systems and anomaly detection to image recognition and natural language processing (NLP). Categories of Machine Learning Algorithms Machine learning algorithms generally fall into three categories: The most powerful large language models (LLMs), which underpin today’s widely used generative AI systems, utilize a combination of these methods, learning from massive datasets. Understanding Generative AI Generative AI models are among the most powerful and popular AI applications, creating new data based on patterns learned from extensive training datasets. These models, which interact with users through conversational interfaces, are trained on vast amounts of internet data, including conversations, interviews, and social media posts. With pre-trained LLMs, users can generate new text, images, audio, and other outputs using natural language prompts, without the need for coding or specialized models. How Does AI Benefit Big Data? AI, combined with big data, is transforming businesses across various sectors. Key benefits include: Big Data and Machine Learning: A Synergistic Relationship Big data and machine learning are not competing concepts; when combined, they deliver remarkable results. Emerging big data techniques offer powerful ways to manage and analyze data, while machine learning models extract valuable insights from it. Successfully handling the various “Vs” of big data enhances the accuracy and power of machine learning models, leading to better business outcomes. The volume of data is expected to grow exponentially, with predictions of over 660 zettabytes of data worldwide by 2030. As data continues to amass, machine learning will become increasingly reliant on big data, and companies that fail to leverage this combination will struggle to keep up. Examples of AI and Big Data in Action Many organizations are already harnessing the power of machine learning-enhanced big data analytics: Conclusion The integration of AI and big data is crucial for organizations seeking to drive digital transformation and gain a competitive edge. As companies continue to combine these technologies, they will unlock new opportunities for personalization, efficiency, and innovation, ensuring they remain at the forefront of their industries. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Salesforce on AI

Salesforce on AI

Marketing success hinges on delivering consistent, timely, and engaging content. According to the Salesforce State of Marketing report, 78% of high-performing marketers identify data as their most critical asset for creating cohesive customer journeys. Yet, only 49% report having a unified view of customer data sources. This disconnect highlights a significant challenge many marketing teams face in effectively leveraging their data. For organizations already invested in Salesforce, incorporating AI-driven business intelligence (BI) tools offers numerous benefits. These include reduced time to deliver insights, enhanced automation, increased innovation, improved agility, and cost savings. However, realizing these benefits depends on having high-quality data and robust data strategies. This insight explores AI-driven BI from a Salesforce perspective, highlighting its benefits, applications, and future trends. By understanding the potential of AI in BI, organizations can better harness their data to drive success and innovation. The Role of AI in Business Intelligence Integrating AI into BI systems elevates data analysis by offering deeper insights and predictive capabilities. Here’s how AI enhances BI: These examples demonstrate AI’s ability to improve BI systems by enhancing data accuracy, providing real-time insights, and improving forecasting. Salesforce’s AI Capabilities in BI Salesforce’s AI capabilities in BI are embodied in the versatile tool, Salesforce Einstein. Easily integrated with BI, Einstein automates tasks and delivers personalized insights. Companies using Einstein have reported a 20% increase in sales productivity. Here’s how Einstein can be utilized in various scenarios: These examples illustrate how Salesforce’s AI tools, particularly Einstein, can transform BI by automating routine tasks and delivering personalized insights, ultimately driving customer satisfaction and business growth. Future Trends in AI and BI The future of AI and BI promises even more advanced capabilities and innovations. As AI evolves, so too will the tools for BI. Here are some trends expected to emerge in the near future: These trends show that AI and BI are evolving rapidly. Companies that stay ahead of these developments will be well-positioned to leverage AI for greater innovation and efficiency. Next Steps AI-powered BI, especially with Salesforce, is transforming how businesses operate by providing deeper insights and better decision-making capabilities. To stay competitive and foster innovation, organizations must embrace AI. The quest is no longer just to be data-driven. It is to be data-decisioned. Here are some actionable steps: By taking these steps, businesses can fully leverage AI-driven BI and maintain a competitive edge in the fast-evolving digital playinf field of AI. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI Assisting Nursing

AI Assisting Nursing

Leveraging AI to Alleviate the Documentation Burden in Nursing As the nursing profession grapples with increasing burnout, researchers are investigating the potential of large language models to streamline clinical documentation and care planning. Nurses play an essential role in delivering high-quality care and improving patient outcomes, but the profession is under significant strain due to shortages and burnout. AI Assisting Nursing could lessoning burnout while improving communication. What role could Salesforce play? The American Nurses Association (ANA) emphasizes that to maximize nurses’ potential, healthcare organizations must prioritize maintaining an adequate workforce, fostering healthy work environments, and supporting policies that back nurses. The COVID-19 pandemic has exacerbated existing challenges, including increased healthcare demand, insufficient workforce support, and a wave of retirements outpacing the influx of new nurses. Tectonic has nearly two decades of experience providing IT solutions for the health care industry. Salesforce, as a leader in the field of artificial intelligence, is a top tool for health care IT. AI Assisting Nursing In response to these growing demands, some experts argue that AI technologies could help alleviate some of the burden, particularly in areaTes like clinical documentation and administrative tasks. In a recent study published in the Journal of the American Medical Informatics Association, Dr. Fabiana Dos Santos, a post-doctoral research scientist at Columbia University School of Nursing, led a team to explore how a ChatGPT-based framework could assist in generating care plan suggestions for a lung cancer patient. In an interview with Healthtech Analytics, Dr. Santos discussed the potential and challenges of using AI chatbots in nursing. Challenges in Nursing Care Plan Documentation Creating care plans is vital for ensuring patients receive timely, adequate care tailored to their needs. Nurses are central to this process, yet they face significant obstacles when documenting care plans. AI Assisting Nursing and Salesforce as a customer relationship solution addresses those challenges. “Nurses are on the front line of care and spend a considerable amount of time interacting closely with patients, contributing valuable clinical assessments to electronic health records (EHRs),” Dr. Santos explained. “However, many documentation systems are cumbersome, leading to a documentation burden where nurses spend much of their workday interacting with EHRs. This can result in cognitive burden, stress, frustration, and disruptions to direct patient care.” The American Association of Critical-Care Nurses (AACN) highlights that electronic documentation is a significant burden, consuming an average of 40% of a nurse’s shift. Time spent on documentation inversely correlates with time spent on patient care, leading to increased burnout, cognitive load, and decreased job satisfaction. These factors, in turn, contribute to patient-related issues such as a higher risk of medical errors and hospital-acquired infections, which lower patient satisfaction. When combined with the heavy workloads nurses already manage, inefficient documentation tools can make care planning even more challenging. AI Assisting Nursing and Care Plans “The demands of direct patient care and managing multiple administrative tasks simultaneously limit nurses’ time to develop individualized care plans,” Dr. Santos continued. “The non-user-friendly interfaces of many EHR systems exacerbate this challenge, making it difficult to capture all aspects of a patient’s condition, including physical, psychological, social, cultural, and spiritual dimensions.” To address these challenges, Dr. Santos and her team explored the potential of ChatGPT to improve clinical documentation. “These negative impacts on a nurse’s workday underscore the urgency of improving EHR documentation systems to reduce these issues,” she noted. “AI tools, if well designed, can improve the process of developing individualized care plans and reduce the burden of EHR-related documentation.” The Promises and Pitfalls of AI Developing care plans requires nurses to draw from their expertise to address issues like symptom management and comfort care, especially for patients with complex needs. Dr. Santos emphasized that advanced technologies, such as generative AI (GenAI), could streamline this process by enhancing documentation workflows and assisting with administrative tasks. AI tools can rapidly process large amounts of data and generate care plans more quickly than traditional methods, potentially allowing nurses to spend more time on direct and holistic patient care. However, Dr. Santos stressed the importance of carefully validating AI models, ensuring that nurses’ clinical judgment and expertise play a central role in evaluating AI-generated care plans. “New technologies can help nurses improve documentation, leading to better descriptions of patient conditions, more accurate capture of care processes, and ultimately, improved patient outcomes,” she said. “This presents an important opportunity to use novel generative AI solutions to reduce nurses’ workload and act as a supportive documentation tool.” Despite the promise of AI as a support tool, Dr. Santos cautioned that chatbots require further development to be effectively implemented in nursing care plans. AI-generated outputs can contain inaccuracies or irrelevant information, necessitating careful review and validation by nurses. Additionally, AI tools may lack the nuanced understanding of a patient’s unique needs, which only a nurse can provide through personal, empathetic interactions, such as interpreting specific cultural or spiritual needs. Despite these challenges, large language models (LLMs) and other GenAI tools are generating significant interest in the healthcare industry. They are expected to be deployed in various applications, including EHR workflows and nursing efficiency. Dr. Santos’ research contributes to this growing field. To conduct the study, the researchers developed and validated a method for structuring ChatGPT prompts—guidelines that the LLM uses to generate responses—that could produce high-quality nursing care plans. The approach involved providing detailed patient information and specific questions to consider when creating an appropriate care plan. The research team refined the Patient’s Needs Framework over ten rounds using 22 diverse hypothetical patient cases, ensuring that the ChatGPT-generated plans were consistent and aligned with typical nursing care plans. “Our findings revealed that ChatGPT could prioritize critical aspects of care, such as oxygenation, infection prevention, fall risk, and emotional support, while also providing thorough explanations for each suggested intervention, making it a valuable tool for nurses,” Dr. Santos indicated. The Future of AI in Nursing While the study focused on care plans for lung cancer, Dr. Santos emphasized that this research is just the beginning of

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Barriers to Healthcare Data Exchange

Barriers to Healthcare Data Exchange

As of 2022, 96% of hospitals were involved in some form of electronic public health data exchange, according to a blog post by the Office of the National Coordinator for Health Information Technology (ONC). Yet Barriers to Healthcare Data Exchange Despite this high engagement, barriers to effective data exchange remain. About three-quarters of hospitals reported facing at least one obstacle to public health reporting. The most frequent challenge was the perception that public health authorities (PHAs) could not receive information electronically. Additionally, hospitals cited technical complexities and exchange costs as significant barriers. ONC data also revealed lower rates of electronic public health reporting among small, rural, independent, and critical access hospitals. Similarly, office-based physicians encounter difficulties in public health data exchange due to limited EHR integration. The 2022 National Physician Health IT Survey indicated that less than half of primary care physicians (41%) used their EHR systems to access immunization data from outside their organizations. Several initiatives are underway to address these public health reporting challenges and improve interoperability between healthcare providers and PHAs: Additionally, a proposed rule from ONC seeks to enhance public health data exchange further. The Health Data, Technology, and Interoperability: Certification Program Updates, Algorithm Transparency, and Information Sharing (HTI-2) proposed rule builds on existing certification criteria by: According to the ONC blog, “Together, these efforts will help address persistent challenges to public health data sharing by investing in public health infrastructure, establishing a governing approach for nationwide health information exchange, and advancing standards to support seamless electronic exchange.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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