Accepted Manuscript. Point: Is ICD-10 diagnosis coding important in the era of big data? Yes. Mark G. Weiner, MD, Assistant Dean, Informatics

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Accepted Manuscript Point: Is ICD-10 diagnosis coding important in the era of big data? Yes Mark G. Weiner, MD, Assistant Dean, Informatics PII: S0012

1 Accepted Manuscript Point: Is ICD-10 diagnosis coding important in the era of big data? Yes Mark G. Weiner, MD, Assistant Dean, Informatics PII: S (18) DOI: /j.chest Reference: CHEST 1538 To appear in: CHEST Received Date: 17 January 2018 Accepted Date: 17 January 2018 Please cite this article as: Weiner MG, Point: Is ICD-10 diagnosis coding important in the era of big data? Yes, CHEST (2018), doi: /j.chest This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

2 Word Count: 1281, 15 References Point: Is ICD-10 diagnosis coding important in the era of big data? Yes Name : Mark G. Weiner, MD Mark.weiner@tuhs.temple.edu Lewis Katz School of Medicine at Temple University Mark Weiner, MD Complete contact address 3440 North Broad Street Philadelphia, PA ` Correspondence to: name and address Mark Weiner, MD 3440 North Broad Street Philadelphia, PA Assistant Dean, Informatics Conflict of interest statement: No Conflicts of Interest to report related to the subject of the manuscript.

3 While the terms ICD10 and big data are relatively new, the goal has been the same for decades: to leverage information collected through the routine operation of the health system to better understand predictors of clinical outcomes, implement data-driven strategies to improve outcomes, 1 and evaluate the changing impact of these strategies as the milieu of healthcare evolves. 2 While new analytic methods have been developed, and computing power has grown exponentially, the fundamental substrate for these analyses is organized data, of which ICD10 coding remains a key component. Therefore, the clear response to the proposition of this debate is a resounding Yes! Typical arguments both for and against the value of ICD10 are extensions of similar arguments made for years about ICD9. The undeniable pro argument was the ubiquity of the codes and their place as a de facto ontological standard in labeling diagnoses required for billing. Many observational analyses have been published using Medicare 3 and Medicaid 4 data which captures longitudinal changes in ICD9 codes on millions of patients over many years. Concerns about risk adjustment have been somewhat addressed by ICD9-based risk models 5,6 that have withstood the test of time. The con arguments are also well-recognized and summarized as (1) codes are collected primarily for billing purposes so their translation to and application within research is inherently flawed 7 and (2) associated clinical terms do not capture the nuance of clinical care, 8 and are inconsistently, inaccurately and incompletely recorded. 9 The migration to ICD10, and the associated increase in the number of codes from 14,000 to 70,000, 10 has offered the potential to capture more granular information about diseases. However, the reason ICD10 diagnosis coding continues to be important in the era of big data is not because the ICD10 codes are so much better than ICD9 they are not. Notably, most of the increase in the number of codes from ICD9 to ICD10 is among orthopedic diagnoses, capturing laterality and proximal/distal location. Aside from a new ability to distinguish acute versus chronic disease, corresponding nuance to common internal medicine terms has not increased proportionally.

4 The reason ICD10 coding remains important is that big data and improved analytics have not yet superseded the fundamental value of a real-time, discretely recorded, provider-based, point-of-care coded assessment of the relevant diseases noted during a clinical encounter. Since the crux of my position is that big data is not ready to replace the value of ICD10 codes, a definition of big data, and an explanation of its strengths and weaknesses seems warranted. Laney was the first to integrate the 3 V s that became the fundamental tenets of Big Data : Volume, Variety, Velocity. 11 Since then, others have added additional V s: Veracity, Validity, Volatility, Visualization and Value. The evolution toward big data reflects the expansion from administrative data sources like Medicare which only meets the Volume criteria, to include sources covering more V s such as labs, medication orders and dispensing, special studies, and real-time telemetry monitoring. Other sources include narrative reports such as clinic notes, or X-rays and pathology reports arising from clinician descriptions or computerized interpretation of raw images. The omics are another source of big data where information on a patient s genomics, epigenomics, transcriptomics, metabolomics, proteomics, and microbiomics may influence health status. Another source includes actively or passively-collected patientreported outcomes and the quantified self, where wearable and mountable sensors are recording data on people as they go about their daily lives. While somewhat Orwellian in their approach, these health and activity monitoring technologies are advancing and people are volunteering for these assessments, evidenced by the NIH Precision Medicine Initiative (PMI) 12 which is set to enroll 1,000,000 patients, and numerous precision medicine institutes opening at major medical centers. Availability of these new sources of big data has been matched by advances in storage, computing power, visualization and analytical approaches that would have been impossible in the 8 bit processing world of the Medicare-data-only days. With all the new sources of data, computing and analytical power, it is tempting, but incorrect, to presume that ICD10 coding is no longer relevant. Big data does not necessarily equate to representative data. 13 Volunteers for the PMI may not be representative of the US population which may still be better represented by ICD10 coding captured through routine health care processes of a

5 comprehensive national cohort. Even apart from the PMI, big data analyses arising from traditional medical sources such as narrative reports may suffer from the well-cited copy/paste issue in clinical notes 14 that can prolong the apparent persistence of clinical findings that have, in reality, changed or even disappeared. Without clinical oversight that includes a well-coded summary assessment of a patient s clinical status, a big data analysis of non-rigorouslycollected data (think respiratory rate 15 ) may reach incorrect conclusions. Big data analyses of high throughput telemetry monitors can assist a clinician in reaching a conclusion about the presence of a condition, but without anchoring on a coded clinical impression, the analysis can be misled by the high rate of false alarms. Defending the value of clinical coding in general, and ICD10 coding in particular can be unpopular in today s world. Much of the hate, misdirected at the coding itself, is more appropriately directed at the billing requirements tightly linked to coding, and the work of doing the coding. With the evolution of payment models from volume to value, the fundamental role of coding as a true representation of patients conditions may be better appreciated. Clinical coding can be supported with big data analysis, where the EHR suggests to the clinician relevant clinical codes based on real-time, computerized review of the clinical notes and associated data. The clinician can then approve, modify or delete these suggested codes. When the accuracy of the suggested codes is such that clinicians add little to the automated coding process, then big data will have superseded the value of manual coding, but the value of assigning, accumulating and using the discrete codes will remain. My support for the ongoing value of clinical coding in general in the era of big data is stronger than my support for ICD10 coding specifically. There is certainly room for improvement in the mechanics and focus of the coding process and in the structure and content of the codes themselves. SNOMED is an even more granular ontology with about 450,000 conceptids, though it includes concept groupings that transcend the diagnosis and procedural domains of ICD10. While one can argue that more granularity supports greater coding accuracy, in practice, that assumption is not always true. The precision of finely-grained terminologies often exceeds the precision of medicine. The appearance of a precise term associated with a

6 patient may imply an undeserved certainty regarding the presence and etiology of disease. For example, when managing a patient with reflux, ICD10 billing rules require specification of the presence or absence of esophagitis which can be presumed, but not known with certainty without additional testing. Many other medical diagnoses in clinical practice are inherently ambiguous, so it would be helpful to indicate a level of clinical concern where the evidence may be incomplete or inconclusive. Therefore, while the evolution of ontologies has been toward greater specificity, I would favor a mechanism to capture the variable degree of certainty with which diagnoses are assigned in actual practice. Such a change would benefit clinicians who can code their impressions more honestly, protect patients who currently carry erroneous diagnoses that were never certain to begin with, and even improve big data analysis that could better calibrate conclusions about the presence of diagnoses on a more continuous basis rather than forcing an artificially and unrealistically rigid present/absent decision. These enhancements would further solidify the continuing role of coding in the era of big data. 1 Elwood PM. Shattuck lecture-outcomes management. N Engl J Med. 1988;318: Collen M.F. (2012) Secondary Medical Research Databases. In: Computer Medical Databases: The First Six Decades ( ). Health Informatics. Springer, London 3 Fung V, Brand R, Newhouse J, Hsu J. Using Medicare Data for Comparative Effectiveness Research Opportunities and Challenges. The American journal of managed care. 2011;17(7): Hennessy S, Freeman CP, Cunningham F. US government claims databases. In: Strom BL, Kimmel SE, Hennessy S, editors. Pharmacoepidemiology. 5 th ed. Chichester: Wiley-Blackwell; p Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. Journal of Clinical Epidemiology 1992;45(6): Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36: Cox E, Martin BC, Van Staa T, Garbe E, Siebert U, Johnson ML. Good Research Practices for Comparative Effectiveness Research: Approaches to Mitigate Bias and Confounding in the Design of Nonrandomized Studies of Treatment Effects Using Secondary Data Sources: The International Society

7 for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report Part II. Value in Health. 2009;12: Hlatky MA. Using Databases to evaluate Therapy. Statistics in Medicine. 1991: 10; Baier AW, Snyder DJ, Leahy, IC, Patak LS, Brustowicz RM. A Shared Opportunity for Improving Electronic Medical Record Data, Anesthesia AND Analgesia. 2017;125: (accessed 11/11/17) 11 Laney D. 3D Data Management: Controlling Data Volume, Velocity, and Variety Volume-Velocity-and-Variety.pdf (accessed 11/11/17) Kaplan RM. Chambers DA. Glasgow RE. Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias. Clin Trans Sci. 2014; Volume 7: Wang MD, Khanna R, Najafi N. JAMA Intern Med. 2017;177(8): Semler MW, Stover DG, Copland Ap, Hong G, Johnson MJ, Kriss MS, Otepka H, Wang L, Christman BW, Rice TW. A Single-Day, Multicenter, Resident-Directed Study of Respiratory Rate. Chest. 2013;143:

Accepted Manuscript. The Challenges of Big Data in Dermatology. Megan H. Noe, MD, MPH, Arash Mostaghimi, MD, MPA, MPH

Accepted Manuscript. The Challenges of Big Data in Dermatology. Megan H. Noe, MD, MPH, Arash Mostaghimi, MD, MPA, MPH Accepted Manuscript The Challenges of Big Data in Dermatology Megan H. Noe, MD, MPH, Arash Mostaghimi, MD, MPA, MPH PII: S0190-9622(18)30664-9 DOI: 10.1016/j.jaad.2018.03.059 Reference: YMJD 12511 To appear