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To the Editor:
I read with interest the article by Sosa and colleagues1 in which they present some stimulating analyses pertaining to a topic that we have been discussing at my institution for several years. Part of this discussion deals with the complexity of our hospital and how complexity is affected by comorbidity coding.
In 2013, we implemented the International Refined-DRGs (IR-DRGs) system to measure complexity at our hospital in Bogotá, Colombia. Our perception at that time was that the case mix index (CMI) was very low (0.7566), even for a general hospital with a high volume of pathologies with low relative weight (RW). Two medical auditors were assigned to review the medical records in order to improve the quality, quantity, and order of diagnoses. Emphasis was placed on patients with stays longer than 5 days and with only 1 diagnosis coded at admission. Additionally, International Classification of Diseases 10th Revision (World Health Organization version) diagnoses from chapters R (Symptoms and Signs Not Elsewhere Classified) and V through Y (External Causes) were blocked in the electronic health record. With these measures, our CMI increased 74%, reaching 1.3151 by the end of 2021, with a maximum peak of 1.6743 in May 2021, which coincided with the third peak of COVID-19 in Colombia.
However, the article by Sosa and colleagues draws my attention to the following: why do the authors state that their CMI is low and the patient acuity was under-represented? Is this due to a comparison with similar hospitals, or to a recommendation from a regulatory agency? We have found our CMI remains low because of a high volume of nonsurgical care (60%), deliveries, and digestive, respiratory, and urinary pathologies of low RW.
Also, was the perceived low CMI causing problems with payers? And further, how did the authors avoid the risk of artificially increasing the CMI through overdiagnosis of patients, and were there audit mechanisms to avoid this? While there was a clear change in expected mortality, did the observed mortality also change with the strategies implemented? This last question is relevant because, if the observed mortality were maintained, this would provide evidence that a coding problem was the cause of their hospital’s low CMI.
I reiterate my congratulations to the authors for presenting analyses that are very useful to other providers and researchers worldwide interested in addressing management issues related to the correct identification and classification of patients.
Carlos Kerguelen, MD, MA
Fundacion Santa Fe de Bogotá, Bogotá, Colombia
carlos.kerguelen@fsfb.org.co
Disclosures: None reported.
1. Sosa M, Ferreira T, Gershengorn H, et al. Improving hospital metrics through the implementation of a comorbidity capture tool and other quality initiatives. J Clin Outcomes Manage. 2022;29(2):80-87. doi:10.12788/jcom.0088
To the Editor:
I read with interest the article by Sosa and colleagues1 in which they present some stimulating analyses pertaining to a topic that we have been discussing at my institution for several years. Part of this discussion deals with the complexity of our hospital and how complexity is affected by comorbidity coding.
In 2013, we implemented the International Refined-DRGs (IR-DRGs) system to measure complexity at our hospital in Bogotá, Colombia. Our perception at that time was that the case mix index (CMI) was very low (0.7566), even for a general hospital with a high volume of pathologies with low relative weight (RW). Two medical auditors were assigned to review the medical records in order to improve the quality, quantity, and order of diagnoses. Emphasis was placed on patients with stays longer than 5 days and with only 1 diagnosis coded at admission. Additionally, International Classification of Diseases 10th Revision (World Health Organization version) diagnoses from chapters R (Symptoms and Signs Not Elsewhere Classified) and V through Y (External Causes) were blocked in the electronic health record. With these measures, our CMI increased 74%, reaching 1.3151 by the end of 2021, with a maximum peak of 1.6743 in May 2021, which coincided with the third peak of COVID-19 in Colombia.
However, the article by Sosa and colleagues draws my attention to the following: why do the authors state that their CMI is low and the patient acuity was under-represented? Is this due to a comparison with similar hospitals, or to a recommendation from a regulatory agency? We have found our CMI remains low because of a high volume of nonsurgical care (60%), deliveries, and digestive, respiratory, and urinary pathologies of low RW.
Also, was the perceived low CMI causing problems with payers? And further, how did the authors avoid the risk of artificially increasing the CMI through overdiagnosis of patients, and were there audit mechanisms to avoid this? While there was a clear change in expected mortality, did the observed mortality also change with the strategies implemented? This last question is relevant because, if the observed mortality were maintained, this would provide evidence that a coding problem was the cause of their hospital’s low CMI.
I reiterate my congratulations to the authors for presenting analyses that are very useful to other providers and researchers worldwide interested in addressing management issues related to the correct identification and classification of patients.
Carlos Kerguelen, MD, MA
Fundacion Santa Fe de Bogotá, Bogotá, Colombia
carlos.kerguelen@fsfb.org.co
Disclosures: None reported.
To the Editor:
I read with interest the article by Sosa and colleagues1 in which they present some stimulating analyses pertaining to a topic that we have been discussing at my institution for several years. Part of this discussion deals with the complexity of our hospital and how complexity is affected by comorbidity coding.
In 2013, we implemented the International Refined-DRGs (IR-DRGs) system to measure complexity at our hospital in Bogotá, Colombia. Our perception at that time was that the case mix index (CMI) was very low (0.7566), even for a general hospital with a high volume of pathologies with low relative weight (RW). Two medical auditors were assigned to review the medical records in order to improve the quality, quantity, and order of diagnoses. Emphasis was placed on patients with stays longer than 5 days and with only 1 diagnosis coded at admission. Additionally, International Classification of Diseases 10th Revision (World Health Organization version) diagnoses from chapters R (Symptoms and Signs Not Elsewhere Classified) and V through Y (External Causes) were blocked in the electronic health record. With these measures, our CMI increased 74%, reaching 1.3151 by the end of 2021, with a maximum peak of 1.6743 in May 2021, which coincided with the third peak of COVID-19 in Colombia.
However, the article by Sosa and colleagues draws my attention to the following: why do the authors state that their CMI is low and the patient acuity was under-represented? Is this due to a comparison with similar hospitals, or to a recommendation from a regulatory agency? We have found our CMI remains low because of a high volume of nonsurgical care (60%), deliveries, and digestive, respiratory, and urinary pathologies of low RW.
Also, was the perceived low CMI causing problems with payers? And further, how did the authors avoid the risk of artificially increasing the CMI through overdiagnosis of patients, and were there audit mechanisms to avoid this? While there was a clear change in expected mortality, did the observed mortality also change with the strategies implemented? This last question is relevant because, if the observed mortality were maintained, this would provide evidence that a coding problem was the cause of their hospital’s low CMI.
I reiterate my congratulations to the authors for presenting analyses that are very useful to other providers and researchers worldwide interested in addressing management issues related to the correct identification and classification of patients.
Carlos Kerguelen, MD, MA
Fundacion Santa Fe de Bogotá, Bogotá, Colombia
carlos.kerguelen@fsfb.org.co
Disclosures: None reported.
1. Sosa M, Ferreira T, Gershengorn H, et al. Improving hospital metrics through the implementation of a comorbidity capture tool and other quality initiatives. J Clin Outcomes Manage. 2022;29(2):80-87. doi:10.12788/jcom.0088
1. Sosa M, Ferreira T, Gershengorn H, et al. Improving hospital metrics through the implementation of a comorbidity capture tool and other quality initiatives. J Clin Outcomes Manage. 2022;29(2):80-87. doi:10.12788/jcom.0088