Showing posts with label patient journey. Show all posts
Showing posts with label patient journey. Show all posts

Wednesday, November 12, 2025

Using data intelligence to simplify the patient journey


Health care delivery faces challenges from fragmented systems and data, impacting patient care and increasing costs. Here's how technology can streamline the patient journey.

Care delivery rarely follows a straight line. Patients must navigate fragmented systems, complicated protocols, and disconnected data sources that leave both them and their providers scrambling to stay on track. Today’s physician groups and practices know firsthand the complexity of this journey.

Physicians who are already operating under tight margins and limited staff can find these additional challenges more than frustrating. They also jeopardize patient care. Missed follow-ups, preventable readmissions, and increased administrative responsibilities can drive up costs, reduce efficiency, and contribute to burnout.

Patients also feel the weight of this system, particularly when it comes to medication adherence. Studies have estimated that non-adherence costs the U.S. health care system between $100 billion and $300 billion annually, with some analyses suggesting $528.4 billion when morbidity and mortality are included. Behind these numbers are real patients who may want to follow instructions but face unscalable barriers ranging from financial hardship to the sheer complexity of managing multiple conditions.


The practice-level impact of fragmentation


When patients fall through the widening cracks in the health care system, practices feel the effects immediately. Staff will spend hours chasing down prior authorizations, clarifying prescriptions, or reconciling records from other providers, resulting in complicated administrative burdens that distract from patient care. There are also revenue pressures, because missed follow-ups and poor adherence can adversely affect value-based reimbursement and increase financial strain. Finally, clinicians experience frustration and moral distress when evidence-based treatment plans are not being carried out, knowing that this often leads to predictable declines in patient health.

As the PAN Foundation explains, cost, complexity and lack of support remain leading drivers of non-adherence. Without proactive systems to address these barriers, practices are left to react in the moment instead of managing care strategically. The result is a cycle of inefficiency that burdens staff, erodes physician well-being, and undermines patient outcomes.


How data science can support practices


Advances in data science and additional technologies are providing physicians and practice teams tools to reduce friction across the patient journey. By consolidating data, surfacing insights, and automating outreach, practices can ease administrative burdens and devote more time to care delivery. This includes:
  • Remote monitoring: The proliferation of connected devices and medtech among the patient population can provide real-time feedback, helping clinicians track patient progress without requiring constant in-office visits.
  • Predictive analytics: Before a patient deviates from their care plan, predictive models can flag those who are at risk of non-adherence, enabling more targeted outreach.
  • Digital reminders and check-ins: Automated messaging tools help patients stay on top of prescriptions and appointments, reducing the need for staff to manually follow up.

These technologies are not meant to replace the provider-patient relationship. Instead, they are meant to extend and augment the reach and efficiency of the practice. The goal is to ensure that a patient’s care plan is adequately supported between visits and that staff time is focused on patients.


What a connected patient journey looks like in practice


For independent practices and clinical research organization alike, creating a “connected” patient journey means thinking beyond isolated points of care. At each stage, data-driven tools can reduce complexity for both patients and staff.

Start from the first step, the diagnosis. Patients can be matched with personalized education and counseling services that are tailored to their specific needs. In this phase, care teams from all sides can gain visibility across the continuum. During treatment, digital adherence platforms can flag early warning signs, such as missed doses or potential side effects, allowing staff to quickly intervene before complications escalate or trials are derailed. Finally, in the ongoing management phase, patients with chronic or acute conditions can receive digital coaching and reminders, which lower the risk of relapse and reduce unnecessary visits. In the end, both patients and practices can benefit from fewer emergencies and practices experience greater predictability in scheduling.

Shifting from a reactive, problem-solving model to a proactive coordination-based approach helps patients feel better supported and frees care teams to focus on care quality instead of administrative triage.

For example, in an oncology study, a mobile health application enabled care providers to monitor patient symptoms and treatment side effects of oral chemotherapy. Over six months, the digital tool not only improved medication adherence but also enhanced multiple quality-of-life measures by enabling earlier clinician interventions and more continuous patient support.
Practical steps for practices

Physicians, clinical research organizations, and patients all know that adopting new technology is not as simple as flipping a switch. Successful implementation requires balancing between setting patient engagement goals with workflow realities, staff capacity, and operational constraints.

Just as in modern clinical research, the process works best when guided by rigor with clear objectives, measurable impact, and validation of outcomes in real-world settings. Still, there are pragmatic steps any practice can take:
  • Start with existing data. Most electronic health records include tools for flagging missed appointments, gaps in refills, or noncompliance with care plans. Treating these indicators like early signals can provide immediate insight into at-risk patients and help identify cohorts for further intervention.
  • Use patient portals. Encouraging portal adoption can streamline communication, improve data capture, and provide a foundation for digital reminders and education, like how clinical studies use digital diaries to track adherence.
  • Establish strategic partnerships. There are numerous parties involved with a patient’s care plan, including payers, pharmacies, and community resources that can address the driving factors of non-adherence. By establishing and building upon these foundational partnerships, research trials can improve patient retention in practice settings, as well.
  • Pilot new tools gradually. Begin with one high-priority population, such as heart failure patients or diabetics, and measure usability/feasibility. Before scaling further, it’s important to allow kinks to be worked out and pave the way for smoother adoption of the next tool. By conducting a pilot study as the first step in an implementation strategy, practices and CROs can test feasibility, gather evidence, and refine workflows before wider deployment.

By treating digital adoption as an evidence-based process, practices can reduce disruption, foster buy-in among staff and patients, and generate valuable real-world data to guide future improvements.


Looking ahead and building evidence for connected care


With advances in artificial intelligence, interoperability, and natural language tools, patient data is no longer locked in silos. Unified datasets enable clearer, more actionable care instructions while value-based reimbursement models strengthen incentives for practices to achieve lasting outcomes instead of short-term fixes.

Even in the age of AI, technology alone is not enough. Success depends on clear goals, consistent data collection, and proactive human support for adherence. Organizations that can apply these evidence-based principles to everyday workflows will play a pivotal role in transforming fragmented care into a connected experience, leading to improved outcomes for patients and reducing administrative strain.

The complexity of today’s patient journey translates into real pressures for both patients and care organizations. With the right digital support, there can be a dynamic shift from chasing details and reactive patient care to focusing on what matters most, delivering high-quality proactive and patient-centric care. In a health care system where nonadherence alone costs hundreds of billions of dollars annually, building a connected patient journey is not just a clinical imperative, but also a practice management strategy that ensures sustainability.


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Tuesday, November 5, 2024

From symptom to treatment: Navigating the patient journey with AI

With health systems worldwide grappling with mounting pressures tied to workforce shortages and staff burnout, emerging technologies – specifically artificial intelligence (AI) integrated with virtual triage and care referral – are emerging as powerful tools to streamline patient care, improve outcomes, and reduce costs. As a medical advisor focused on AI applications in healthcare, I have been integrally involved in research and development that is transforming the patient journey, with a particular focus on care acuity alignment and early detection of critical conditions.

Recent research has broken new ground in two areas: care acuity alignment with respect to care de-escalation and escalation, and early detection of leading chronic diseases, including heart attack, stroke, pulmonary embolism, pneumonia, and asthma.


Aligning care with patient acuity levels


AI plays a crucial role in helping patients navigate the complex healthcare landscape, especially when they are uncertain about their care needs. AI has value for patients who are unclear about whether they need care in the first place, and if they do, what level of care is appropriate. Through virtual triage encounters, AI-powered systems can assess symptoms, generate potential diagnoses, and recommend appropriate levels of care acuity.

A recent study published in the International Journal of Healthcare evaluated the impact of AI-based virtual triage and care referral (VTCR) on patient care intent and seeking in an ambulatory setting. Researchers analyzed 8,088 online encounters to understand how VT influenced patient behavior in engaging various levels of care acuity. The technology is designed to evaluate patients’ care intentions and to help align patients with the appropriate care that is needed based on their clinical presentation. VTCR was found to reduce unnecessary in-person visits and promote virtual care among patients seeking care in a leading ambulatory care system.

Among the results:
  • A 19.1% increase in patients opting for virtual care options such as e-visits and telephone consultations.
  • 12.5% decrease in outpatient care seeking, including in-person and video consultations.
  • 35% of all patients altered their care seeking behaviors following VT recommendations, and among patients whose care intent differed from VT, 50% altered their care seeking in alignment with the recommendation of VTCR.

VTCR technology reduces patient uncertainty by providing clear guidance on whether self-care at home is sufficient, if a visit to their regular physician is necessary and sufficient, or if urgent care and emergency department attention is required. The AI explains its recommendations, helping patients understand the rationale behind the suggested care path.


Early detection of critical conditions


One of the most promising applications of AI in healthcare is its ability to facilitate early detection of potentially life-threatening conditions. Recent research has focused on five high-morbidity, high-mortality conditions: heart attack, stroke, asthma, pneumonia, and pulmonary embolism.

Findings revealed a significant disconnect between patients’ self-perceived acuity levels and the actual urgency of their conditions as determined by AI-based virtual triage. Substantial numbers of individuals did not intend before triage to seek the level of urgent care that they needed clinically.

By identifying these discrepancies, AI can prompt patients to seek appropriate care sooner, potentially saving lives and reducing long-term health consequences. For conditions like stroke and heart attack (myocardial infarction), where time is critical, early intervention facilitated by AI VTCR could mean the difference between long-term disability and a full recovery.


Enhancing clinical decision-making


For healthcare providers, AI-powered virtual triage systems offer valuable insights that can expedite and improve clinical decision-making. By providing a ranked list of potential diagnoses based on the patient’s reported symptoms, AI gives clinicians a head start in their assessment.

It saves time and helps to organize information, offering a clinical workflow advantage by potentially expediting the ordering of therapeutics and delivery of care to the patient. This efficiency not only benefits the healthcare system but also ensures patients receive timely, appropriate care.


Challenges and considerations


While the potential of AI in healthcare is vast, its implementation comes with challenges. Healthcare providers and administrators may initially approach AI with skepticism, requiring education and transparency about the technology’s capabilities and limitations. It is crucial to prioritize patient safety in AI design. For instance, some systems are built to slightly “over-triage” to emergency departments, erring on the side of caution rather than risking under-diagnosis of serious conditions.

Looking ahead, several exciting developments in AI-powered healthcare are on the horizon:Integration of objective clinical data: 
  • As more patients use devices like pulse oximeters and blood pressure monitors at home, AI systems will incorporate this data to enhance diagnostic accuracy and care recommendations.
  • Improved language models: Advancements in large language models (LLMs) will increase the fluidity and power of AI in healthcare applications as well as increasing patient comfort and satisfaction with VTCR.
  • Expanded condition coverage: AI systems will become more adept at identifying and managing rarer conditions and mental health issues.
  • Addressing socially stigmatized conditions: AI may provide a more comfortable platform for patients to discuss sensitive health issues like sexually transmitted diseases or substance abuse.
  • Focus on chronic disease management: As the leading source of morbidity and mortality in most nations, an increasing focus on chronic diseases in AI-based VTCR will play a larger role in long-term patient care and monitoring.
  • Tackling healthcare access and resource inequities: AI-powered virtual triage can improve healthcare access for underserved, high inequity populations, addressing long standing disparities in care.

We are at the beginning of the healthcare journey with AI-based VTCR. As AI continues to evolve, its ability to align care with patient needs, detect critical conditions earlier, refer patients for needed care, and support clinical decision-making will increase dramatically.

AI is poised to revolutionize the patient’s and the clinician’s journey, from initial symptom assessment to long-term care management. By leveraging VTCR technology to improve care acuity alignment and early detection of serious conditions, healthcare providers and plans can enhance patient outcomes, reduce costs, and create a more efficient, equitable healthcare system for all. For senior healthcare executives, embracing and integrating these AI technologies will be crucial in staying at the forefront of patient care and operational efficiency and organizational performance in the coming years.

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