Patient Scoring in Critical Care: Why Global Standards Aren’t So Global

Patient scoring systems like SOFA, APACHE, and TISS are often perceived as globally consistent tools for assessing patient severity and guiding clinical decisions. But a closer look reveals that the global standards we use (or rather, think we use) aren’t so global after all.

This common misconception arises from their widespread use, and the assumption that their regimented calculations ensure uniformity across different healthcare settings. However, the reality is far more complex.

The global standard that isn’t

While these scoring systems were indeed designed to standardize care and offer objective metrics for patient evaluation, national and local adaptations have led to diverse interpretations and implementations, with individual regions and even hospitals having their own take. Acknowledging and addressing these differences is crucial for benchmarking, improving international collaboration, and the pursuit of ever-improving global healthcare standards.

What you will learn

• An overview of patient scoring
• Local adaptations of global scores
• Challenges for clinicians when facing non-globalized standards
• Implications for clinical information management
• How to accommodate scoring standard differences

The importance of global scoring in critical care

Global scoring in critical care is vital as it provides a standardized, objective way to assess patient severity and predict outcomes, enabling consistent comparisons and improved resource allocation across different institutions and regions. It also facilitates research and quality improvement initiatives by allowing for the analysis of large datasets and the identification of best practices in critical care.

On a day-to-day basis, scoring impacts clinicians and hospital managers significantly, covering essential areas of care such as prioritization and staff planning. The statistics generated are also used for reimbursement purposes, as well as guiding and supporting operational performance.

Types of patient scoring in critical care

In critical care, there are two general types of patient scoring used:

• Workload scoring, which measures the complexity of care and influences reimbursement rates.
• Patient risk scoring, which predicts mortality risk based on diagnoses, comorbidities, and clinical measurements.

 
To assess them, several scores are commonly used, including SOFA, APACHE, TISS, and SAPS, which are based on clinical research.

Why global scoring standards vary by location

Despite often being referred to as global standards, these scoring standards may vary significantly between countries, regions, and even hospitals.

Healthcare authorities in different districts, for example, often modify scoring criteria to align with local clinical practices, resource availability, and epidemiological factors. These adjustments can significantly alter the application and outcomes of these scores, leading to notable variations in patient classification and treatment protocols.

Reasoning behind some of these differences

To understand how these differences may evolve, it’s important to understand the parameters or reasoning that guide varying local interpretations. For instance:

• The APACHE score, which assesses the severity of disease in intensive care units, may use different laboratory thresholds or weighting systems depending on the country or even the institution.
• SOFA scores, intended to evaluate organ dysfunction, can be adapted to reflect local guidelines on sepsis management or specific patient population characteristics.
• TISS scores, which measure nursing workload, might be adjusted based on staffing models and care delivery standards unique to a particular healthcare system.

 
These local adaptations, while often necessary to reflect regional realities, complicate efforts to compare patient outcomes and care quality across borders. They also challenge the notion of a truly standardized approach, emphasizing the importance of understanding context when interpreting scores.

Practical examples of scoring system variations

A good practical demonstration is the disparity in SAPS III, a common risk score used to predict hospital mortality, to which several local adjustments have been made across the globe.

The original structure of the SAPS III score, for instance, mentions the need to include data recorded within one hour of admission to the ICU. This has been shown in studies to calculate the risk more accurately than other prediction models¹. However, the Austrian Social Affairs, Health, Care and Consumer Protection Ministry deems from one hour before ICU admission until six hours after to be sufficient for collecting data for SAPS III assessment.

The Swedish Intensive Registry, for its part, allows hospitals to use the RLS 85 scale as an option for determining coma severity², while SAPS III requires the registration of coma severity to be based on the Glasgow Coma Scale (GCS).

“Worst value” score deviations and the implications

Some scores are designed for use in non-technological environments, which can be problematic when they are applied in more computerized settings.

For example, most scoring systems request the patient’s “worst” value of specific clinical measurements. Without software, this is a highly subjective assessment on the part of the clinician. In the case of heart rate measurement, for instance, a clinician may not take the worst value if they deem it irrelevant, such as if the measuring equipment was not properly connected, or the patient was in a state where the values didn’t make sense. On the other hand, if a computerized system that collects data every minute is used, the term “worst value” becomes more difficult to define, as it must make sense from a programming point of view, which could by default include such outlier scenarios.

Hidden complexities that impact clinicians

Clinicians may be unaware of the variations and assume they follow a single global standard when, in fact, they follow a localized version. This can impact their work and the hospital’s operations, particularly in these areas:

• Data consistency in research and benchmarking as clinical outcomes may be skewed when comparing patients across organizations.
• Reimbursement calculations when compared and/or collected across countries or states.
• Communication barriers when transferring patients between facilities that use different clinical guidelines for the same scoring system.

 
To avoid confusion and potential errors, it is important for clinicians to understand their local guidelines and how they may differ from the guidelines in other countries.

Where patient scoring meets information management systems

A clinical information system (CIS) can standardize data input and scoring calculations, reducing clinician confusion and errors across different scoring systems.

The essential starting point is to ensure the chosen system vendor is aware of local adaptations. A company that operates in the international field, with implementations in hospitals worldwide is preferable, as it will be more versed in local scoring demands to accommodate your needs. Plus, its development team will be innately operating with the mindset and coding practices to support different interpretations of patient scoring.

Beyond that, customization is crucial. That is, the system should be flexible, designed with customizable algorithms and rules engines that allow local administrators to modify scoring algorithms, add or remove variables, and adjust weighting based on local protocols or research findings.

How iMDsoft can help

MetaVision, iMDsoft’s clinical information system, is inherently suited to meet the challenges posed by non-global patient scoring in critical care. iMDsoft’s coding and mindset are aligned with this reality, and the solution itself is very flexible, with many local adaptations for different markets already implemented.

Key takeaways

• Patient scoring systems like SOFA, APACHE, and TISS are widely used but are not truly global; local adaptations significantly affect their application and outcomes.
• Differences in patient scoring arise due to national and regional adjustments that align with local clinical practices, resource availability, and healthcare policies.
• These variations challenge data consistency, research benchmarking, and communication across institutions.
• A Clinical Information System (CIS) can help bridge these gaps by standardizing data input, supporting local adaptations, and improving clarity in patient assessment.
• iMDsoft’s MetaVision is designed to address these local differences, offering customizable algorithms and rules engines. Additionally, many local configurations have already been successfully implemented.

Conclusion: Rethinking global patient scoring standards

The reality is that while global standards exist, patient scoring systems are often highly localized. The resulting variations can hinder consistent data analysis and cross-institutional collaboration, limiting our ability to establish universal best practices through research. Evolving healthcare technologies, diverse patient populations, and mobility of patients between hospitals and regions necessitate more adaptable and inclusive scoring models.

Healthcare providers, and ultimately patients, would be better served with increased awareness, plus adaptation of data management systems that seamlessly accommodate these diversities, to remove any ambiguity or miscommunication.

 

FAQs

• What is patient scoring?

  Patient scoring systems provide a standardized, objective method to quantify patient conditions, aiding in risk assessment and treatment planning. They utilize predefined parameters to generate numerical scores, allowing clinicians to track patient progress and compare outcomes across populations. These scores facilitate informed decision-making, resource allocation, and research, ultimately enhancing patient care.

• Is patient scoring standardized?

  Patient scoring systems strive for standardization with defined parameters, but complete uniformity is hindered by implementation variations, subjective parameters, and local adaptations. While many components are standardized, differences in usage and the sheer volume of scoring systems create a degree of variability, despite the goal of objective assessment.

• How does a clinical information system support patient scoring?

  A clinical information system significantly enhances patient scoring through automated data capture and integration. With a flexible design, it can employ rules and algorithms to accommodate for potential scoring variations in different locations, to create a uniform basis for assessment.

1. https://academic.oup.com/bja/article/101/6/798/249987

2. https://www.icuregswe.org/globalassets/riktlinjer/riskjustering.pdf