Redesigning a Classic: The Review of Systems, Reimagined
The Review of Systems has been a clinical staple for decades. We gave it an upgrade
What is a Review of Systems?
At its core, a Review of Systems (“ROS”) is a top-to-bottom assessment of a patient’s physical status. it is, by design, a systematic checklist (doctors love a checklist), with the primary goal of providing a consistent and thorough starting point from which the provider can begin treatment: starting large (which body systems are affected?) and narrowing to specific issues and concerns the patient may be facing.
The logic flows in two directions:
Large to small: Reviews begin with location-specific systems—cardiovascular, respiratory, gastrointestinal—before drilling down into discrete symptoms within each.
Broad to specific: The review targets general conditions first, then narrows based on patient responses. A “yes” to fatigue opens different pathways than a “yes” to chest pain.
This structure exists for good reason: it provides a standardized starting point that any clinician—NP, PA, or MD—can work from.
Why It Matters
The ROS serves as the foundation for the entire patient journey; it influences diagnoses, treatments considered, and ongoing care that may be necessary. If it’s done poorly, everything else that happens afterwards is affected.
A data scientist would call this garbage-in, garbage-out. The same principle applies in the clinic: a rushed or incomplete ROS doesn’t just slow things down, it undermines ultimate success.
The Problem with the Status Quo
Traditionally, ROS has been rote, generic, and one-size-fits-all for most patients and encounters. The same questions, with mild variations, regardless of what we already know about a patient, which is usually minimal anyway.
Updating a Classic
We’re now able to do something different.
Starting with the Data
It begins with the data. For patients presenting for complex treatment, we can now prefill more than half of a ROS, usually the boring bits, before the encounter.
When does this work:
To find data, data must exist; there’s no crystal ball for a patient’s health. A healthy patient who doesn’t require much treatment will likely only benefit mildly from this; however, patients undergoing complex treatment have typically engaged extensively with the healthcare system beforehand. You don’t jump straight to complex work for many reasons, risk and cost being the primary two. As patients move through the levels of treatment triage, more and more is learned. The amount of data, particularly recent data, we have on a patient grows proportionally, so much so that we have an inside joke at the office that almost anyone on our team, regardless of training, can determine someone’s ASA score simply by looking at how much recent data we have on a patient.
When you’re in the American healthcare system, you don’t expect things to move quickly... and then when it starts to get a little bit efficient, it’s actually quite disconcerting. — Hank Green
In the past, those records were used sporadically, if at all. Now, we can fully capitalize on the work of previous providers, and the benefits compound:
Faster treatment: Less time spent re-asking questions the patient has already answered
Better care: Clinicians start from a more complete baseline, with more time for the questions that actually matter
Data is only the beginning
Having access to comprehensive patient data is necessary, but far from sufficient. The real work begins when data is combined, cross-referenced, and thoughtfully processed against preferenced-guidelines. Patient histories scattered across different providers, labs, imaging centers, and specialists need to be reconciled to remove conflicting information and standardized for efficient use.
How we accomplish this is a topic we’ll address in depth later. For now, know that the Orchestra Clinical Data Network gives us the raw ingredients to build new journeys.
Responsible use of LLMs within care journeys
A cornerstone of efficiently augmenting ROS today lies in fundamentally understanding how best to use LLMs to both structure and detect relevant information. That conerstoner includes a core understanding that LLMs are not intelligent and are fundamentally an autocomplete tool, albeit an incredibly powerful one.
Where we’ve seen success
Detection: As fuzzy searchers, LLMs are the pinnacle of modern implementation. They can surface relevant information buried in thousands of pages of clinical documentation. They handle misspellings, abbreviations, and the idiosyncratic shorthand that litter clinical documentation.
Structuring: We’ve built systems that ingest large volumes of unstructured data and, with human guidance, rapidly transform it into structured, actionable information. Free-text notes become coded diagnoses. Scattered observations become timelines. This structured output then feeds deterministic systems that can apply consistent clinical logic at scale.
Where we’ve seen inconsistencies
Prescriptive Language: LLMs love telling us what to do, and if you let it, it will happily and confidently develop and regurgitate a complete care plan for any individual patient chart you point it at, with or without guidance. EMRs are, in a very real way, legal records, which makes prescriptive outputs from LLMs at best risky, and at worst, fraud.
Not all regulations are bad
We said that there is no crystal ball for a patient’s health, which might lead one to wonder why this data hadn’t been used previously. Honestly, it simply wasn’t accessible.
Until recently, leveraging data outside of your system (and sometimes even within the system) Much access to this data was typically walled off within private EMRs, under the guise of privacy and protection, which complicated sharing of data even within a single system, however, in 2021 we started to see the beginnings of change, through the implementation of the Cures Act which was signed into law in 2016 and went into effect in 2022.
Brief Summary The 21st Century Cures Act mandated the creation of TEFCA (Trusted Exchange Framework and Common Agreement), a nationwide framework for secure health information exchange. TEFCA establishes a standard set of technical and legal requirements to enable different health information networks to connect and share data seamlessly across the country.
Who It Impacts
Healthcare providers and hospital systems
Health information exchanges (HIEs)
Health IT vendors and EHR developers
Patients accessing care across different networks
Qualified Health Information Networks (QHINs) participating in the framework
Benefits
Enables nationwide health data exchange across previously disconnected networks
Reduces burden on providers by standardizing exchange requirements
Improves patient care continuity when receiving treatment from multiple organizations
Streamlines access to patient records across state lines
Reduces duplicative testing and administrative costs
Creates a single on-ramp for organizations to connect to various networks
Better foundations lead to better results
The ROS is a critical cornerstone of effective medical practice, but its potential and efficacy have always been limited by the tools available. Coupling clean, structured data with thoughtfully deployed systems enables more effective treatment with far less effort or time.