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
Sarcastic Critique of the STORM Study
Ah, the marvel of modern science: transforming the whimsical world of subjective thoughts into cold, complex, objective data.
The VHA Stratification Tool for Opioid Risk Mitigation (STORM) is nothing short of an Icarian attempt by the government to criminalize mere inklings of danger before they materialize. Let’s delve into this extraordinary endeavor where crystal balls meet clinical settings, shall we?
Context of the Innovation
“STORM was developed and implemented in accordance with federal law and in the context of years of relatively high rates of opioid prescribing to veterans and overall rising opioid-involved overdose mortality in the United States, where deaths have more than doubled in the last 10 years.16
In response to high rates of overdose and opioid-related harm, in 2016 Congress passed CARA, which requires providers to improve opioid therapy strategies and to ensure responsible prescribing practices.
STORM is one of many national opioid safety initiatives implemented in the last 5 to 10 years. In 2013, the VHA launched the Opioid Safety Initiative to promote the safe and effective use of opioid analgesics. In addition to STORM, under the Opioid Safety Initiative, the VHA has worked to distribute naloxone, reduce opioid prescribing, and implement use of pain management teams.17
There were other screening tools to identify high-risk patients in use at the time of the implementation of the STORM predictive model; however, the STORM team sought to improve the usability and accuracy of these tools. With these aims, the STORM team incorporated data from the VHA EHR and more sophisticated and rapid data processing into the calculation of risk scores. Additionally, STORM was designed for a specific population (i.e., veterans) with mitigation strategies that are intended to be actionable and easily implemented into routine clinical processes.18“
Predicting the Future: A Nostalgic Affair
Lag in Data Availability: The multi-year delay in data on the cause of death is a charming throwback to the times when waiting was an art. Who needs current data anyway? You might as well be planning a eulogy when you act on the predictions.
Coding System Transitions: The shift from ICD-9 to ICD-10 is akin to translating Shakespeare into modern slang. Despite best efforts, nuances are lost, and errors are introduced. But fear not, for we are in predictive analytics, where precision is a quaint concept.
Static Risk Models: Why bother with tailored risk models when a one-size-fits-all approach suffices? After all, humans are practically identical, and variations in subpopulations are mere figments of our collective imagination.
Evolving Medical Practices: Who would have thought that medical practices evolve? Predictive models based on historical data assume that the past is a perfect predictor of the future. Revolutionary.
From Subjective to Objective: A Leap of Faith
Lack of Contextual Information: The exclusion of nuanced contextual information from structured EMR data is a masterstroke. Who needs context when you have data points? Let’s simplify complex human behaviors into neat little variables and call it a day.
Simplification of Complex Variables: Converting opioid doses into morphine-equivalent daily doses (MEDD) without considering overlapping prescriptions or dose changes is a stroke of genius. After all, simplicity is the ultimate sophistication, especially when it leads to overestimating or underestimating risk.
Risk Scores and Quality of Care: Using high-risk scores to reflect clinical history rather than care quality is a brilliant move. It ensures that misinterpreting risk scores as indicators of poor care quality remains a perpetual mystery, adding an element of surprise to patient safety.
Potential for Misclassification: Misclassifying patients is just a minor hiccup in this grand scheme. Identifying 7.9% of the cohort incorrectly as high-risk adds a touch of randomness to life, making the predictive model more exciting.
Over-Reliance on Historical Data: Because history never repeats itself, over-relying on past data to predict future events is perfectly logical. Changes in patient demographics, behaviors, and medical practices are just minor details in the grand tapestry of prediction.
The Objective Illusion
Converting subjective thoughts into objective measures is the pièce de résistance of the STORM initiative. With methods like quantification and inter-subjective agreement, subjective experiences are forced into the rigid molds of objectivity.
Quantification: Measuring subjective experiences using standardized tools or scales brings a pseudo-scientific charm to the endeavor. Why rely on personal bias when you can use surveys and psychometrics to feign objectivity?
Inter-Subjective Agreement: Achieving consensus in subjective experiences gives the illusion of objectivity. If multiple people report the same feeling, it must be true, right? It’s a delightful way to mask the variability of human perception.
Operationalization: Defining abstract concepts in measurable terms, like intelligence through IQ tests, ensures that complex human traits are reduced to mere numbers. It epitomizes scientific reductionism, making life easier for those who prefer black-and-white answers.
Conclusion
The STORM initiative is a shining example of how the government, like Icarus, aspires to convert subjective musings into objective certainties, often with the grace of a bull in a china shop.
The inherent challenges of predicting future events and converting subjective assessments into objective metrics are mere trifles in this noble quest.
With data lags, coding transitions, and an over-reliance on historical data, the STORM model valiantly attempts to bring order to the chaotic realm of human experience.
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