A patient chart rarely hides information because it is missing. More often, it is scattered across previous visits, medication changes, referrals, test results, clinical notes and documentation created by different care teams.
Finding the right detail can therefore become work of its own.
A descriptive study published in the Annals of Internal Medicine analysed approximately 100 million outpatient encounters involving more than 155,000 physicians. It found that physicians spent an average of 16 minutes and 14 seconds using the EHR per encounter, with chart review accounting for 33% of that time.
A separate 2024 review in Frontiers in Digital Health examined the growing burden of patient chart review and the potential role of AI clinical summarisation. The authors noted that physicians can spend approximately 4.5 hours per day working in EHR systems, with about one-third of that time related to reviewing patient charts.
This makes physician time spent searching EHR charts more than a small workflow inconvenience.
AI-assisted documentation offers an opportunity to improve not only how clinical documentation is recorded but also how easily that information can be understood and retrieved later.
Here are seven chart-search bottlenecks AI can help reduce.
1. Reconstructing the Reason Behind Today’s Visit
The latest clinical note does not always tell the full story.
A patient may be returning because of a symptom first recorded three visits ago, a medication adjustment made last month or a specialist recommendation buried in an earlier encounter.
Without sufficient context, clinicians may need to move backwards through the chart and manually reconstruct how the patient arrived at the current point in care.
AI can make that process more efficient by organising relevant historical information before or during the visit.
Penn Medicine’s Chart Hero, for example, was developed to help clinicians gather, organise, and synthesise information already contained in the EHR before seeing a patient.
Instead of treating every previous encounter as equally important, systems like this aim to surface the information most relevant to the current clinical question.
Notiro follows a similar workflow principle by bringing previous visits, summaries and relevant patient history closer to the documentation process.
As a leading AI-assisted clinical documentation solution, Notiro helps healthcare teams reduce unnecessary chart navigation by making historical context easier to access during the encounter. This can save time and reduce the chance that important information is overlooked while switching between multiple sections of the record.
2. Searching Several Notes for One Clinical Fact
Sometimes the clinician does not need the entire patient history. They need one answer.
- When was the medication changed?
- What did the previous specialist recommend?
- Has this symptom appeared before?
- Was a particular test already completed?
Traditional EHR navigation often requires clinicians to guess which note or tab contains the answer and then search manually. AI introduces a different model.
Stanford Health Care’s ChatEHR pilot allows clinicians to interact with patient records using natural-language questions. Stanford reported that clinicians could request chart summaries and retrieve specific information from patient histories rather than manually searching individual records.
This type of AI patient record search automation can shift the workflow from searching documents to asking clinical questions.
Importantly, Stanford describes ChatEHR as an information-gathering tool rather than a replacement for medical judgment.
That distinction matters. AI can make information easier to locate. The clinician remains responsible for interpreting what that information means.
3. Navigating Through Too Many EHR Screens
Chart review is not difficult only because of the amount of information involved. It can also be difficult because of where that information lives. A typical review might involve moving between:
patient history → previous encounters → medications → laboratory results → imaging → referrals → clinical notes.
Each step may take only seconds. Across dozens of patients, those seconds accumulate.
The Annals of Internal Medicine study helps put that burden into perspective. If chart review represents approximately one-third of physician EHR activity during an encounter, reducing unnecessary navigation could return meaningful time to the clinical workflow.
This is where organisations looking to reduce EHR chart navigation time should think beyond faster documentation alone.
Notiro uses AI to help bring patient history and current documentation into a more connected workflow. By reducing the need to repeatedly move between separate sources of information, healthcare teams can spend less time navigating the record and more time evaluating the patient in front of them.
4. Re-entering Information That Already Exists
Search and documentation often overlap. A clinician finds an old piece of information, copies it into a new note and re-enters details that already exist elsewhere in the record. This creates two problems.
- The first is time.
- The second is the possibility of introducing inconsistencies or errors through repeated manual entry.
AI-supported workflows can reduce this duplication by carrying relevant context forward and structuring information as it enters the clinical record.
Notiro brings AI into multiple stages of the workflow. Patient information can be gathered before the appointment while clinical conversations can be converted into structured documentation during the encounter.
Connecting those stages helps teams streamline repetitive tasks and reduces opportunities for manual errors caused by retyping, copying or transferring information between systems.
This is an important part of AI clinical chart retrieval efficiency. Faster search matters, but preventing unnecessary re-entry can make the entire information flow more efficient.
5. Searching Notes That Were Never Designed for Retrieval
A clinical note can be complete and still be difficult to search.
Important information may sit inside long paragraphs alongside details that are no longer relevant.
Medication changes, treatment decisions, symptoms and follow-up instructions may all be present, but identifying them can still require careful reading.
The 2024 Frontiers in Digital Health review highlights this broader problem. As patient records continue to grow, clinicians face increasing information volume during chart review. The authors identify AI summarisation as one potential way to reduce the burden of navigating large and complex patient records.
This means documentation quality and retrieval efficiency are closely connected. Notiro uses AI to transform clinical conversations into organised documentation, helping healthcare teams create clearer records while reducing manual documentation effort.
Better structured documentation today can make tomorrow’s chart easier to understand.
That is one of the most sustainable ways for AI to reduce time searching patient charts: improve how clinical information is captured before clinicians ever need to search for it again.
6. Connecting Information Across Multiple Encounters
Some of the most useful patient context exists across several visits rather than within one note. Consider a patient with recurring headaches.
- In March, the symptom appears for the first time.
- In April, medication is prescribed.
- In May, side effects are documented.
- In August, the medication changes.
By the next appointment, the clinically relevant story is spread across four different encounters. Manual review requires the clinician to connect those pieces mentally.
AI can help organise longitudinal information so recurring symptoms, previous decisions and changes over time become easier to recognise.
Notiro’s patient-history capabilities support this type of continuity by making previous visits and relevant clinical context accessible alongside the current documentation workflow.
This does not mean AI determines what the pattern means. It means AI can help assemble the evidence more efficiently so clinicians have stronger context when making decisions. That distinction is critical to responsible clinical AI.
7. Treating Documentation and Retrieval as Separate Problems
Healthcare organisations often evaluate documentation and chart search as two different workflow challenges.
In reality, they are part of the same information cycle.
What is collected before the visit affects the encounter.
What is documented during the encounter affects future chart review.
How consistently that information is structured affects how quickly another clinician can understand it later.
Notiro brings AI into this broader information workflow. By helping teams capture patient information, organise clinical documentation, and retain useful historical context, Notiro can reduce repetitive work while improving the efficiency with which information moves from one stage of care to another.
This is where AI becomes more than a documentation shortcut. It can help healthcare teams streamline processes, reduce avoidable manual errors and reach relevant information more efficiently.
The result is not simply faster note creation. It is a more usable patient record.
Faster Chart Retrieval Still Requires Clinical Oversight
Speed should never be the only measure of a clinical AI system. Healthcare teams evaluating AI-assisted chart retrieval should also ask:
- Can clinicians verify where important information came from?
- Does the system fit into the existing clinical workflow?
- Can AI-generated information be reviewed before it influences care?
- Does the technology reduce steps or simply create another system clinicians must navigate?
Stanford explicitly positions ChatEHR as a tool for gathering information while leaving clinical decision-making to healthcare professionals.
Penn Medicine has taken a similar approach with Chart Hero. The system is designed to connect AI-generated statements with information from the underlying chart so clinicians can review the source data themselves.
These safeguards matter because AI clinical chart retrieval efficiency should never come at the expense of accuracy or clinical accountability.
The best AI workflows make information easier to access while keeping healthcare professionals in control.
From Searching the Chart to Understanding the Patient
The next opportunity in clinical AI is not simply generating notes faster. It is making the information inside those notes easier to use.
For healthcare teams asking how AI can reduce time searching patient charts, the answer involves the entire information journey: capture relevant context, structure it clearly, connect it with patient history and make important details easier to retrieve when they matter.
Notiro is built around that connected approach. By using AI to streamline documentation, organise patient information and bring historical context closer to the point of care, Notiro helps healthcare teams reduce time spent navigating fragmented records and minimise avoidable workflow errors.
AI does not replace clinical decision-making. It can reduce the work required to reach the information behind those decisions.
And in a workflow where clinicians already spend a significant portion of their EHR time reviewing charts, making that information easier to reach can make a meaningful difference.
