How AI helps physician practices turn complexity into clarity

Physician practices face mounting pressure from administrative demands, growing patient complexity, and rising expectations for coordination across care settings. These challenges affect clinicians across the health care system, yet they are felt most acutely at the front lines, where decisions must be made quickly and with confidence.

As a practicing emergency physician, I experience this reality every shift. Emergency departments operate under constant time pressure, staffing constraints, and high patient acuity. Clinicians make critical decisions with incomplete information while managing interruptions, handoffs, and documentation requirements. The lessons learned in this environment offer valuable insights for primary care, where similar data challenges play out over longer timelines and across multiple encounters.

After all, in EDs and practices, the volume of clinical data received each day continues to grow, much of which offers little support during the moments when decisions need to be made. This manifests in primary care physicians spending roughly one‑third to one‑half of each patient visit working in the EHR rather than in direct patient interaction, according to a 2025 study. This imbalance limits the time available for meaningful clinical discussions and contributes to burnout.

Growing care fragmentation intensifies the data and time management challenges. For example, the share of Medicare beneficiaries who visited five or more providers each year increased from 17.5% in 2000 to 30.1% in 2019. Each additional provider increases the likelihood of incomplete information, duplicated work, and delayed follow-up.

Independent practices are likely to continue to navigate these realities. However, AI is increasingly offering a path to greater clarity by helping physicians organize and interpret the large volumes of information surrounding every patient encounter. These tools support clinical judgment by highlighting the details that matter most and by fitting naturally into established workflows that practices already rely on.

The information problem in everyday practice

In the ED, the cost of unclear or delayed information becomes immediately apparent. Patients often arrive from other care settings or from home with paper packets, faxes, or partial records that provide limited context. Clinicians must rapidly determine what happened before arrival, what matters now, and what comes next.

Physicians in their practices encounter a parallel challenge. Referral notes arrive without clear clinical questions. Discharge summaries appear days after patients return home. Medication lists require reconciliation across multiple sources. These inefficiencies consume time and introduce risk.

This situation has deep roots in the way health care information has historically been shared, which I can attest to personally as the son of a career-long independent neurologist. In his practice, my father frequently accepted referred patients without receiving essential clinical details about why they were being sent or what had already been tried. He also encountered large packets of information that offered little real insight. Independent physicians still encounter similar scenarios despite massive advances in technology.

I previously wrote here on how this burden affects care delivery. Fragmented information leads to delays, unnecessary utilization, and frustration for both patients and clinicians. These challenges heighten the need for complete, concise, and clinically relevant information so physicians can make timely and confident decisions.

Supporting decision-making and care continuity

Emergency medicine has always demanded efficiency. Clinicians must rapidly synthesize data, identify risks, and act decisively. Over the past 15 years, I have seen how better data presentation can change clinical practice.

AI can help clinicians reduce complexity by summarizing and structuring information in ways that directly support clinical reasoning. These tools create opportunities for improvements. Small gains in clarity and speed can make an entire practice feel more manageable.

In the two emergency departments I practice in, AI-supported summaries have changed how quickly care teams can assess incoming patients from skilled nursing facilities. Within moments, we can view recent changes in vital signs, treatment updates, and care plans. These insights reduce duplicative testing, accelerate evaluation, and support safer decisions about whether patients can return to their prior setting.

Predictive insights further enhance decision-making. Risk-scoring models help clinicians identify individuals more likely to deteriorate after an acute event. This capability supports earlier intervention and strengthens the coordination between acute, post-acute, and primary care providers. These models guide attention toward patients at increased risk, helping reduce preventable readmissions.

Yet at the same time, AI also must align with responsible principles. Physicians should be able to understand how outputs were generated, confirm the underlying documentation, and retain full authority over care decisions. Transparency and validation build confidence and support safe use across diverse patient populations.

Preparing for a connected, cross-continuum future

EDs often serve as the intersection point between acute, post-acute, and community-based care. Gaps in visibility across these settings contribute to avoidable readmissions and repeated emergency visits.

Likewise, practices need visibility into patient movement across settings, including discharge timing, medication changes, recent interventions, and urgent concerns that require rapid follow-up. In turn, timely insight would strengthen continuity and help practices intervene before avoidable complications arise.

As the urgency grows around these needs, the broader health care landscape is steadily moving toward more connected and data-informed workflows. AI-supported tools can assist by bringing together standardized, clinically relevant information from hospitals, post-acute facilities, and home-based providers. This type of visibility allows primary care teams to care for patients with more confidence and helps families feel more supported.

Practices considering such technologies to support future care delivery should look for solutions that ease administrative load, integrate with existing workflows, and support information sharing across care settings. They should incorporate responsible AI standards, offer transparency, and ensure that physicians remain fully aware of how insights are generated and retain sole authority over all clinical decisions and actions.

Building momentum through small changes

Independent practices can begin adopting AI to improve information flow through targeted use cases that reduce friction and support everyday decisions. Incremental progress can reshape clinician experience and strengthen patient outcomes. As more of the care continuum adopts connected, coordinated models supported by AI, practices that embrace these tools can align more easily with partners and participate more fully in an integrated health care environment.

This momentum positions practices to benefit from the same kind of clarity that my colleagues and I rely on every day in the emergency department. When information arrives in a timely, structured, and clinically relevant way, it supports faster decisions, safer transitions, and more proactive care planning across settings.

Used thoughtfully, AI can help physicians in the community apply lessons long familiar to the emergency department by working with the right information at the right moment to deliver care that feels more coordinated, more anticipatory, and more aligned with the needs of every patient they serve.

Hamad Husainy, DO, FACEP, is a practicing emergency medicine physician and the chief medical officer of PointClickCare.