NORMAN J CLEMENT RPH., DDS, NORMAN L. CLEMENT PHARM-TECH, MALACHI F. MACKANDAL PHARMD, BELINDA BROWN-PARKER, IN THE SPIRIT OF JOSEPH SOLVO ESQ., INC.T. SPIRIT OF REV. C.T. VIVIAN, JELANI ZIMBABWE CLEMENT, BS., MBA., IN THE SPIRIT OF THE HON. PATRICE LUMUMBA, IN THE SPIRIT OF ERLIN CLEMENT SR., WALTER F. WRENN III., MD., JULIE KILLINGWORTH, LESLY POMPY MD., CHRISTOPHER R> RUSSO, MD., AISHA GARNER, NANCY SEEFELDT, WILLIE GUINYARD BS., JOSEPH WEBSTER MD., MBA, BEVERLY C. PRINCE MD., FACS., NEIL ARNAND, MD., RICHARD KAUL, MD., IN THE SPIRIT OF LEROY BAYLOR, JAY K. JOSHI MD., MBA, ADRIENNE EDMUNDSON, ESTER HYATT PH.D., WALTER L. SMITH BS., IN THE SPIRIT OF BRAHM FISHER ESQ., MICHELE ALEXANDER MD., CUDJOE WILDING BS, MARTIN NJOKU, BS., RPH., IN THE SPIRIT OF DEBRA LYNN SHEPHERD, BERES E. MUSCHETT, STRATEGIC ADVISORS
“..The U.S. Government’s war on doctors is seen by many as a grave transgression. As we navigate the murky waters of legal battles, the timeless wisdom of one of the world’s greatest physicians, John Locke, serves as a guiding light. His philosophy of individual rights and limited government provides a rallying cry for defending the rights of medical practitioners against governmental overreach..”
Dr. Richard Red Lawhearn, PhD’s Critique of “STORM“
The document focuses on the development and application of the VHA Stratification Tool for Opioid Risk Mitigation (STORM), which uses predictive modeling to identify patients at risk for overdose or suicide-related events.
While the initiative aims to improve patient safety and resource allocation, several issues and limitations are inherent in the process of predicting future events and converting subjective assessments into objective metrics.
Problems with Predicting the Future
- Lag in Data Availability:
- There is a multiple-year lag in the availability of cause of death data, which affects the application of risk parameters from earlier years to current data(Storm Study). This delay can result in outdated risk assessments that do not reflect recent trends or changes in patient conditions.
- Coding System Transitions:
- The transition from ICD-9 to ICD-10 coding systems introduces discrepancies in diagnostic definitions(Storm Study). Although efforts are made to match ICD-10 definitions to ICD-9 categories, this change can introduce errors and inconsistencies in the data.
- Static Risk Models:
- The document acknowledges the potential benefits of multiple tailored risk models for specific subpopulations(Storm Study). However, the current model does not account for such specificity, which could improve accuracy. A one-size-fits-all model might not accurately capture the nuanced risk profiles of diverse patient groups.
- Evolving Medical Practices:
- Medical practices and patient behaviors evolve over time. Predictive models based on historical data may not adequately capture these changes, leading to inaccuracies in risk predictions.
Subjective
- Definition: Subjective refers to perspectives, feelings, or opinions that are influenced by personal experiences and emotions. It is inherently personal and can vary from one individual to another.
- Characteristics:
- Personal Bias: Subjective statements are often influenced by personal bias.
- Variability: Different people can have different subjective experiences or opinions about the same situation.
- Examples:
- “Chocolate ice cream is the best flavor.” (Opinion)
- “I feel happy today.” (Personal experience)
- “This painting is beautiful.” (Aesthetic judgment)
“..Pain medications are not, contrary to a false media and litigation narrative, addictive. In addition, addiction is not the same as tolerance and/or dependence—they are two different molecular structures. This has been emphasized by the National Institute of Drug Abuse (NIDA)..”
Objective
- Definition: Objective refers to facts, observations, and truths that exist independently of individual thoughts or feelings. These are considered to be unbiased and universally valid.
- Characteristics:
- Impartiality: Objective statements aim to be free of personal bias or emotions.
- Consistency: Objective facts remain consistent regardless of who observes them.
- Examples:
- “Water boils at 100 degrees Celsius at standard atmospheric pressure.” (Scientific fact)
- “The population of New York City is over 8 million.” (Demographic fact)
- “The Earth orbits the Sun.” (Astronomical fact)
Converting Subjective to Objective
Converting subjective statements or experiences into objective ones can be challenging but is sometimes possible through methods that aim to remove personal biases and rely on measurable, verifiable data. Here are a few ways this can be approached:
- Quantification: By measuring subjective experiences using standardized tools or scales, it is possible to make them more objective. For example:
- Surveys and Questionnaires: Well-designed surveys will be used to gather data on subjective experiences (e.g., happiness, satisfaction), and the results will then be analyzed statistically.
- Psychometrics: Tools like the Likert scale can quantify subjective opinions and feelings, making them easier to analyze objectively.
- Inter-Subjective Agreement: When multiple individuals independently report the same subjective experience, it can lend credibility to the objectivity of the observation. For instance:
- Consensus in Research: If many people independently report that a particular therapy reduces anxiety, this inter-subjective agreement can be used to form an objective conclusion about the therapy’s effectiveness.
- Operationalization: Defining subjective concepts in terms of observable and measurable operations can help in making them objective. For example:
- Defining Constructs: In psychology, abstract concepts like intelligence are operationalized through IQ tests, which provide measurable data.
- Controlled Experiments: Conducting experiments under controlled conditions can help isolate and measure subjective experiences objectively. For example:
- Placebo-Controlled Trials: In medicine, subjective reports of pain relief can be objectively measured by comparing the effects of a drug to a placebo in controlled trials.
While complete objectivity may not always be achievable, these methods can help bridge the gap between subjective experiences and objective analysis.
Errors in Turning Subjective into Objective
- Lack of Contextual Information:
- The risk models are based on structured EMR data extracts, which do not include nuanced contextual information about patients(Storm Study). Important factors captured in free-text clinical notes are excluded, potentially reducing the model’s accuracy.
- Simplification of Complex Variables:
- Predictive models often reduce complex clinical decisions and patient behaviors to simplified variables. For example, the opioid dose is converted to morphine equivalent daily dose (MEDD) without accounting for overlapping prescriptions and dose changes over time(Storm Study). This simplification can lead to overestimation or underestimation of risk.
- Risk Scores and Quality of Care:
- The document highlights that high-risk scores reflect patient clinical history rather than the quality of care received(Storm Study). This distinction is crucial, as it prevents misinterpretation of risk scores as indicators of poor care quality. However, it also underscores the challenge of objectively assessing risk without considering the quality and context of care.
- Potential for Misclassification:
- The predictive model can misclassify patients, identifying some as high-risk incorrectly(Storm Study). For instance, targeting the top 100,000 patients with the highest risk scores captures only about half of those who will experience an overdose or suicide-related event while incorrectly identifying 7.9% of the cohort.
- Over-Reliance on Historical Data:
- Historical data may not accurately predict future events due to changes in patient demographics, behaviors, and medical practices. Over-reliance on past data can lead to predictions that do not align with current realities.
Conclusion
While the STORM initiative represents a significant advancement in leveraging predictive analytics for patient safety, it also illustrates the inherent challenges in predicting future events and converting subjective clinical assessments into objective measures.
These challenges include data lags, coding transitions, the static nature of risk models, and the simplification of complex variables. Addressing these issues requires continuous updates to the model, incorporation of contextual information, and a nuanced understanding of risk scores and their implications for patient care.
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