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George S, Campbell N, Hillman SL, Harlos ES, Stein DWJ, Chan MY, Chow SL, Elrahi CL, Quina AC, Kokolus MC, Casagni MD, Weiss M, Anderson DM, Stadler WM, Hoff OC, Rivera DR, Kluetz PG, Mandrekar SJ, Piantadosi S. Feasibility of structuring electronic health record data to facilitate real-world data research: ICAREdata methods applied to multicenter cancer clinical trials. Cancer 2024. [PMID: 39192753 DOI: 10.1002/cncr.35528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 06/14/2024] [Accepted: 07/15/2024] [Indexed: 08/29/2024]
Abstract
BACKGROUND The use of electronic health record (EHR) data for research is limited by a lack of structure and a standard data model. The objective of the ICAREdata (Integrating Clinical Trials and Real-World Endpoints Data) project was to structure key research data elements in EHRs using a minimal Common Oncology Data Elements (mCODE) data model to extract and transmit data. METHODS The ICAREdata project captured two EHR data elements essential to clinical trials: cancer disease status and treatment plan change. The project was implemented in clinical sites participating in Alliance for Clinical Trials in Oncology trials. Data were extracted from EHRs and sent by secure Fast Healthcare Interoperability Resource messaging (a standard for exchanging EHRs) to a database. Selected elements were compared with corresponding data from the trial's electronic data capture (EDC) system, Medidata Rave. RESULTS By December 2023, data were extracted and transmitted from 10 sites for 35 patients, involving 367 clinical encounters across 15 clinical trials. Data through March 2023 demonstrated that concordance for the elements treatment plan change and cancer disease status was 79% and 34%, respectively. When disease evaluation was reported by both EHR and EDC (n = 15), there was 87% agreement on cancer disease status. CONCLUSIONS Documentation, extraction, and aggregation of structured data elements in EHRs using mCODE and ICAREdata methods is feasible in multi-institutional cancer clinical trials. EDC as a reference data set allowed assessment of the completeness of EHR data capture. Future initiatives will focus on elements with shared definitions in clinical and research environments and efficient workflows. PLAIN LANGUAGE SUMMARY Clinical trials use electronic case report forms to report data, and data must be manually entered on these forms, which is costly and time consuming. ICAREdata methods use structured, organized data from clinical trials that can be more easily shared instead having to enter free text into electronic health records.
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Affiliation(s)
- Suzanne George
- Dana-Farber/Harvard Cancer Center, Boston, Massachusetts, USA
| | - Nancy Campbell
- Data Innovation Lab LLC, Alliance for Clinical Trials in Oncology, Boston, Massachusetts, USA
| | - Shauna L Hillman
- Alliance Statistics and Data Management Center, Mayo Clinic, Rochester, Minnesota, USA
| | - Elizabeth S Harlos
- Alliance Statistics and Data Management Center, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Selina L Chow
- Alliance for Clinical Trials in Oncology, Chicago, Illinois, USA
| | - Cassandra L Elrahi
- Data Innovation Lab LLC, Alliance for Clinical Trials in Oncology, Boston, Massachusetts, USA
| | | | | | | | - Matthias Weiss
- ThedaCare Regional Cancer Center, Appleton, Wisconsin, USA
| | - Daniel M Anderson
- Metro-Minnesota Community Oncology Research Consortium, St Louis Park, Minnesota, USA
| | - Walter M Stadler
- University of Chicago Comprehensive Cancer Center, Chicago, Illinois, USA
| | - Olivia C Hoff
- Metro-Minnesota Community Oncology Research Consortium, St Louis Park, Minnesota, USA
| | - Donna R Rivera
- Oncology Center of Excellence, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Paul G Kluetz
- Oncology Center of Excellence, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Sumithra J Mandrekar
- Alliance Statistics and Data Management Center, Mayo Clinic, Rochester, Minnesota, USA
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Dennstädt F, Putora PM, Cihoric N. (Common) Data Elements in Radiation Oncology: A Systematic Literature Review. JCO Clin Cancer Inform 2023; 7:e2300008. [PMID: 37369089 DOI: 10.1200/cci.23.00008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 04/22/2023] [Accepted: 04/28/2023] [Indexed: 06/29/2023] Open
Abstract
PURPOSE Structured medical data documentation is highly relevant in a data-driven discipline such as radiation oncology. Defined (common) data elements (CDEs) can be used to record data in clinical trials, health records, or computer systems for improved standardization and data exchange. The International Society for Radiation Oncology Informatics initiated a project for a scientific literature analysis of defined data elements for structured documentation in radiation oncology. METHODS We performed a systematic literature review on both PubMed and Scopus to analyze publications relevant to the utilization of specified data elements for the documentation of radiation therapy (RT)-related information. Relevant publications were retrieved as full-text and searched for published data elements. Finally, the extracted data elements were quantitatively analyzed and classified. RESULTS We found a total of 452 publications, of which 46 were considered relevant for structured data documentation. Twenty-nine publications addressed defined RT-specific data elements, of which 12 publications provided data elements. Only two publications focused on data elements in radiation oncology. The 29 analyzed publications were heterogeneous regarding the subject and usage of the defined data elements, and different concepts/terms for defined data elements were used. CONCLUSION The literature about structured data documentation in radiation oncology using defined data elements is scarce. There is a need for a comprehensive list of RT-specific CDEs the radio-oncologic community can rely on. As it has been done in other medical fields, establishing such a list would be of great value for clinical practice and research as it would promote interoperability and standardization.
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Affiliation(s)
- Fabio Dennstädt
- Department of Radiation Oncology, Kantonsspital St Gallen, St Gallen, Switzerland
| | - Paul Martin Putora
- Department of Radiation Oncology, Kantonsspital St Gallen, St Gallen, Switzerland
- Department of Radiation Oncology, University of Bern, Bern, Switzerland
| | - Nikola Cihoric
- Department of Radiation Oncology, University of Bern, Bern, Switzerland
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Adesoye T, Katz MHG, Offodile AC. Meeting Trial Participants Where They Are: Decentralized Clinical Trials as a Patient-Centered Paradigm for Enhancing Accrual and Diversity in Surgical and Multidisciplinary Trials in Oncology. JCO Oncol Pract 2023; 19:317-321. [PMID: 36821820 DOI: 10.1200/op.22.00702] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/24/2022] [Accepted: 12/15/2022] [Indexed: 02/25/2023] Open
Affiliation(s)
- Taiwo Adesoye
- Department of Breast Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Matthew H G Katz
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Anaeze C Offodile
- Institute for Cancer Care Innovation, University of Texas MD Anderson Cancer Center, Houston, TX
- Department of Plastic and Reconstructive Surgery, University of Texas MD Anderson Cancer Center, Houston, TX
- Baker Institute for Public Policy, Rice University, Houston, TX
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4
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Dolin RH, Heale BSE, Alterovitz G, Gupta R, Aronson J, Boxwala A, Gothi SR, Haines D, Hermann A, Hongsermeier T, Husami A, Jones J, Naeymi-Rad F, Rapchak B, Ravishankar C, Shalaby J, Terry M, Xie N, Zhang P, Chamala S. Introducing HL7 FHIR Genomics Operations: a developer-friendly approach to genomics-EHR integration. J Am Med Inform Assoc 2022; 30:485-493. [PMID: 36548217 PMCID: PMC9933060 DOI: 10.1093/jamia/ocac246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/16/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE Enabling clinicians to formulate individualized clinical management strategies from the sea of molecular data remains a fundamentally important but daunting task. Here, we describe efforts towards a new paradigm in genomics-electronic health record (HER) integration, using a standardized suite of FHIR Genomics Operations that encapsulates the complexity of molecular data so that precision medicine solution developers can focus on building applications. MATERIALS AND METHODS FHIR Genomics Operations essentially "wrap" a genomics data repository, presenting a uniform interface to applications. More importantly, operations encapsulate the complexity of data within a repository and normalize redundant data representations-particularly relevant in genomics, where a tremendous amount of raw data exists in often-complex non-FHIR formats. RESULTS Fifteen FHIR Genomics Operations have been developed, designed to support a wide range of clinical scenarios, such as variant discovery; clinical trial matching; hereditary condition and pharmacogenomic screening; and variant reanalysis. Operations are being matured through the HL7 balloting process, connectathons, pilots, and the HL7 FHIR Accelerator program. DISCUSSION Next-generation sequencing can identify thousands to millions of variants, whose clinical significance can change over time as our knowledge evolves. To manage such a large volume of dynamic and complex data, new models of genomics-EHR integration are needed. Qualitative observations to date suggest that freeing application developers from the need to understand the nuances of genomic data, and instead base applications on standardized APIs can not only accelerate integration but also dramatically expand the applications of Omic data in driving precision care at scale for all.
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Affiliation(s)
- Robert H Dolin
- Corresponding Author: Robert H. Dolin, MD, Elimu Informatics, 1709 Julian Ct, El Cerrito, CA 94530, USA;
| | | | - Gil Alterovitz
- Brigham and Women’s Hospital, Boston, Massachusetts, USA,Harvard/MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA
| | - Rohan Gupta
- Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| | | | | | - Shaileshbhai R Gothi
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - David Haines
- Leap of Faith Technologies, Libertyville, Illinois, USA
| | - Arthur Hermann
- Department of Health IT Strategy & Policy, Kaiser Permanente, Pasadena, California, USA
| | | | - Ammar Husami
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - James Jones
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, USA
| | | | | | | | | | - May Terry
- MITRE Corporation, McLean, Virginia, USA
| | - Ning Xie
- Biomedical Cybernetics Laboratory, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Powell Zhang
- Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Srikar Chamala
- Keck School of Medicine, Department of Pathology, University of Southern California, Los Angeles, California, USA,Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California, USA
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5
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Ciabattoni A, Gregucci F, D’Ermo G, Dolfi A, Cucciarelli F, Palumbo I, Borghesi S, Gava A, Cesaro GM, Baldissera A, Giammarino D, Daidone A, Maurizi F, Mignogna M, Mazzuoli L, Ravo V, Falivene S, Pedretti S, Ippolito E, Barbarino R, di Cristino D, Fiorentino A, Aristei C, Ramella S, D’Angelillo RM, Meattini I, Iotti C, Donato V, Formenti SC. Patterns of Care for Breast Radiotherapy in Italy: Breast IRRadiATA (Italian Repository of Radiotherapy dATA) Feasibility Study. Cancers (Basel) 2022; 14:3927. [PMID: 36010920 PMCID: PMC9405796 DOI: 10.3390/cancers14163927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 11/16/2022] Open
Abstract
Aim. Breast IRRADIATA (Italian Repository of RADIotherapy dATA) is a collaborative nationwide project supported by the Italian Society of Radiotherapy and Clinical Oncology (AIRO) and the Italian League Against Cancer (LILT). It focuses on breast cancer (BC) patients treated with radiotherapy (RT) and was developed to create a national registry and define the patterns of care in Italy. A dedicated tool for data collection was created and pilot tested. The results of this feasibility study are reported here. Methods. To validate the applicability of a user-friendly data collection tool, a feasibility study involving 17 Italian Radiation Oncology Centers was conducted from July to October 2021, generating a data repository of 335 BC patients treated between January and March 2020, with a minimum follow-up time of 6 months. A snapshot of the clinical presentation, treatment modalities and radiotherapy toxicity in these patients was obtained. A Data Entry Survey and a Satisfaction Questionnaire were also sent to all participants. Results. All institutions completed the pilot study. Regarding the Data Entry survey, all questions achieved 100% of responses and no participant reported spending more than 10 min time for either the first data entry or for the updating of follow-up. Results from the Satisfaction Questionnaire revealed that the project was described as excellent by 14 centers (82.3%) and good by 3 (17.7%). Conclusion. Current knowledge for the treatment of high-prevalence diseases, such as BC, has evolved toward patient-centered medicine, evidence-based care and real-world evidence (RWE), which means evidence obtained from real-world data (RWD). To this aim, Breast IRRADIATA was developed as a simple tool to probe the current pattern of RT care in Italy. The pilot feasibility of IRRADIATA encourages a larger application of this tool nationwide and opens the way to the assessment of the pattern of care radiotherapy directed to other cancers.
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Affiliation(s)
- Antonella Ciabattoni
- Department of Radiation Oncology, Ospedale San Filippo Neri, ASL Roma 1, 00135 Roma, Italy
| | - Fabiana Gregucci
- Department of Radiation Oncology, Ospedale Generale Regionale “F. Miulli”, Acquaviva delle Fonti, 70021 Bari, Italy
| | - Giuseppe D’Ermo
- Department of Surgery, “Pietro Valdoni”, Universitá di Roma “La Sapienza”, 00185 Roma, Italy
- LILT, Lega Italiana Contro i Tumori, Sede Centrale Via A. Torlonia, 15, 00161 Roma, Italy
| | - Alessandro Dolfi
- Department of Radiation Oncology, Ospedale San Filippo Neri, ASL Roma 1, 00135 Roma, Italy
| | - Francesca Cucciarelli
- Department of Radiation Oncology, Azienda Ospedaliera Ospedali Riuniti Marche Nord, 61029 Ancona, Italy
| | - Isabella Palumbo
- Radiation Oncology Section, University of Perugia and Perugia General Hospital, 06123 Perugia, Italy
| | - Simona Borghesi
- Department of Radiation Oncology, Azienda USL Toscana Sud Est, Sede Operativa Valdarno, 52100 Arezzo Valdarno, Italy
| | - Alessandro Gava
- Department of Radiation Oncology, Azienda Ospedaliera ULSS 9, 31100 Treviso, Italy
| | | | | | - Daniela Giammarino
- Department of Radiation Oncology, Azienda Ospedaliera San Camillo Forlanini, 00152 Roma, Italy
| | - Antonino Daidone
- Department of Radiation Oncology, Centro San Gaetano, Sede di Bagheria e Sede di Mazzara del Vallo, 35121 Palermo e Trapani, Italy
| | - Francesca Maurizi
- Department of Radiation Oncology, Azienda Ospedaliera Ospedali Riuniti Marche Nord, 61029 Pesaro, Italy
| | - Marcello Mignogna
- Department of Radiation Oncology, Azienda Ospedaliera USL Toscana Nord Ovest, 56121 Lucca, Italy
| | - Lidia Mazzuoli
- Department of Radiation Oncology, Azienda Ospedaliera ASL Viterbo, 01100 Viterbo, Italy
| | - Vincenzo Ravo
- Department of Radiation Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, 80131 Napoli, Italy
| | - Sara Falivene
- Department of Radiation Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, 80131 Napoli, Italy
| | - Sara Pedretti
- Department of Radiation Oncology, ASST Spedali Civili Brescia, 25123 Brescia, Italy
| | - Edy Ippolito
- Department of Radiation Oncology, Università Campus Bio-Medico e Fondazione Campus Bio-Medico, 00128 Roma, Italy
| | - Rosaria Barbarino
- Department of Radiation Oncology, Fondazione PTV, Policlinico Tor Vergata, 75013 Roma, Italy
| | - Daniela di Cristino
- Department of Radiation Oncology, Ospedale San Filippo Neri, ASL Roma 1, 00135 Roma, Italy
| | - Alba Fiorentino
- Department of Radiation Oncology, Ospedale Generale Regionale “F. Miulli”, Acquaviva delle Fonti, 70021 Bari, Italy
| | - Cynthia Aristei
- Radiation Oncology Section, University of Perugia and Perugia General Hospital, 06123 Perugia, Italy
| | - Sara Ramella
- Department of Radiation Oncology, Università Campus Bio-Medico e Fondazione Campus Bio-Medico, 00128 Roma, Italy
| | | | - Icro Meattini
- Department of Experimental and Clinical Biomedical Sciences “M. Serio”, Department of Oncology, Radiation Oncology Unit, Ospedale Universitario Careggi, Universitá di Firenze, 50134 Firenze, Italy
| | - Cinzia Iotti
- Radiation Therapy Unit, Azienda USL—IRCCS di Reggio Emilia, 42122 Reggio Emilia, Italy
- AIRO President, AIRO-Associazione Italiana di Radioterapia ed Oncologia Clinica, Piazza della Repubblica 32, 20124 Milano, Italy
| | - Vittorio Donato
- Department of Radiation Oncology, Azienda Ospedaliera San Camillo Forlanini, 00152 Roma, Italy
- AIRO Past President, AIRO-Associazione Italiana di Radioterapia ed Oncologia Clinica, Piazza della Repubblica 32, 20124 Milano, Italy
| | - Silvia Chiara Formenti
- Department of Radiation Oncology, Weill Cornell Medicine—New York Presbyterian Hospital, New York, NY 10065, USA
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6
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Keegan NM, Vasselman SE, Barnett ES, Nweji B, Carbone EA, Blum A, Morris MJ, Rathkopf DE, Slovin SF, Danila DC, Autio KA, Scher HI, Kantoff PW, Abida W, Stopsack KH. Clinical annotations for prostate cancer research: Defining data elements, creating a reproducible analytical pipeline, and assessing data quality. Prostate 2022; 82:1107-1116. [PMID: 35538298 PMCID: PMC9246896 DOI: 10.1002/pros.24363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/04/2022] [Accepted: 04/20/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND Routine clinical data from clinical charts are indispensable for retrospective and prospective observational studies and clinical trials. Their reproducibility is often not assessed. We developed a prostate cancer-specific database for clinical annotations and evaluated data reproducibility. METHODS For men with prostate cancer who had clinical-grade paired tumor-normal sequencing at a comprehensive cancer center, we performed team-based retrospective data collection from the electronic medical record using a defined source hierarchy. We developed an open-source R package for data processing. With blinded repeat annotation by a reference medical oncologist, we assessed data completeness, reproducibility of team-based annotations, and impact of measurement error on bias in survival analyses. RESULTS Data elements on demographics, diagnosis and staging, disease state at the time of procuring a genomically characterized sample, and clinical outcomes were piloted and then abstracted for 2261 patients (with 2631 samples). Completeness of data elements was generally high. Comparing to the repeat annotation by a medical oncologist blinded to the database (100 patients/samples), reproducibility of annotations was high; T stage, metastasis date, and presence and date of castration resistance had lower reproducibility. Impact of measurement error on estimates for strong prognostic factors was modest. CONCLUSIONS With a prostate cancer-specific data dictionary and quality control measures, manual clinical annotations by a multidisciplinary team can be scalable and reproducible. The data dictionary and the R package for reproducible data processing are freely available to increase data quality and efficiency in clinical prostate cancer research.
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Affiliation(s)
- Niamh M. Keegan
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Ethan S. Barnett
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Barbara Nweji
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Emily A. Carbone
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Alexander Blum
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Michael J. Morris
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
- Weill Cornell Medical College, New York, NY
| | - Dana E. Rathkopf
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
- Weill Cornell Medical College, New York, NY
| | - Susan F Slovin
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
- Weill Cornell Medical College, New York, NY
| | - Daniel C. Danila
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
- Weill Cornell Medical College, New York, NY
| | - Karen A. Autio
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
- Weill Cornell Medical College, New York, NY
| | - Howard I. Scher
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
- Weill Cornell Medical College, New York, NY
| | - Philip W. Kantoff
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
- Weill Cornell Medical College, New York, NY
| | - Wassim Abida
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
- Weill Cornell Medical College, New York, NY
- Correspondence: Wassim Abida and Konrad Stopsack, Department of Medicine, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Phone (646) 422-4633, and
| | - Konrad H. Stopsack
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
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7
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Emamekhoo H, Carroll CB, Stietz C, Pier JB, Lavitschke MD, Mulkerin D, Sesto ME, Tevaarwerk AJ. Supporting Structured Data Capture for Patients With Cancer: An Initiative of the University of Wisconsin Carbone Cancer Center Survivorship Program to Improve Capture of Malignant Diagnosis and Cancer Staging Data. JCO Clin Cancer Inform 2022; 6:e2200020. [PMID: 35802837 DOI: 10.1200/cci.22.00020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Structured data elements within electronic health records are health-related information that can be entered, stored, and extracted in an organized manner at later time points. Tracking outcomes for cancer survivors is also enabled by structured data. We sought to increase structured data capture within oncology practices at multiple sites sharing the same electronic health records. METHODS Applying engineering approaches and the Plan-Do-Study-Act cycle, we launched dual quality improvement initiatives to ensure that a malignant diagnosis and stage were captured as structured data. Intervention: Close Visit Validation (CVV) requires providers to satisfy certain criteria before closing ambulatory encounters. CVV may be used to track open clinical encounters and chart delinquencies to encourage optimal clinical workflows. We added two cancer-specific required criteria at the time of closing encounters in oncology clinics: (1) the presence of at least one malignant diagnosis on the Problem List and (2) staging all the malignant diagnoses on the Problem List when appropriate. RESULTS Six months before the CVV implementation, the percentage of encounters with a malignant diagnosis on the Problem List at the time of the encounter was 65%, whereas the percentage of encounters with a staged diagnosis was 32%. Three months after cancer-specific CVV implementation, the percentages were 85% and 75%, respectively. Rates had increased to 90% and 88% more than 2 years after implementation. CONCLUSION Oncologist performance improved after the implementation of cancer-specific CVV criteria, with persistently high percentages of relevant malignant diagnoses and cancer stage structured data capture 2 years after the intervention.
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Affiliation(s)
- Hamid Emamekhoo
- University of Wisconsin, Madison, WI.,Carbone Cancer Center, Madison, WI
| | | | | | | | | | | | - Mary E Sesto
- University of Wisconsin, Madison, WI.,Carbone Cancer Center, Madison, WI
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8
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Torous VF, Allan RW, Balani J, Baskovich B, Birdsong GG, Dellers E, Dryden M, Edgerton ME, Giannico GA, Heayn M, Jackson CR, Klepeis VE, Olson JE, Pettus JR, Simpson RW, Sirintrapun SJ, Smith DL, Srigley JR, Berman MA. In Reply. Arch Pathol Lab Med 2022; 146:141b-143. [DOI: 10.5858/arpa.2021-0461-le] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/08/2021] [Indexed: 11/06/2022]
Affiliation(s)
- Vanda F. Torous
- Department of Pathology, Massachusetts General Hospital, Boston
| | - Robert W. Allan
- Department of Pathology, Malcom Randall Veterans Affairs Medical Center, University of Florida, Gainesville
| | - Jyoti Balani
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas
| | - Brett Baskovich
- Department of Pathology, University of Florida College of Medicine, Jacksonville
| | - George G. Birdsong
- Department of Pathology, Emory University School of Medicine at Grady Hospital, Atlanta, Georgia
| | | | - Mignon Dryden
- North American Association of Central Cancer Registries, Portland, Oregon
| | - Mary E. Edgerton
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston
| | - Giovanna A. Giannico
- Department of Pathology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Michelle Heayn
- Department of Pathology, Einstein Medical Center Montgomery, East Norriton, Pennsylvania
| | | | | | - Jordan E. Olson
- Department of Pathology, Geisinger Medical Center, Danville, Pennsylvania
| | - Jason R. Pettus
- Department of Pathology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
| | - Ross W. Simpson
- Department of Pathology, Park Nicollet-Methodist Hospital, St Louis Park, Minnesota
| | | | | | - John R. Srigley
- Department of Pathology, Trillium Health Partners and University of Toronto, Toronto, Ontario, Canada
| | - Michael A. Berman
- Department of Pathology, Jefferson Hospital, Allegheny Health Network, Jefferson Hills, Pennsylvania
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9
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Schorer AE, Moldwin R, Koskimaki J, Bernstam EV, Venepalli NK, Miller RS, Chen JL. Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality. JCO Clin Cancer Inform 2022; 6:e2100128. [PMID: 34985912 PMCID: PMC9848533 DOI: 10.1200/cci.21.00128] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/27/2021] [Accepted: 11/15/2021] [Indexed: 01/26/2023] Open
Abstract
PURPOSE The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) requires eligible clinicians to report clinical quality measures (CQMs) in the Merit-Based Incentive Payment System (MIPS) to maximize reimbursement. To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS CQMs, EHR data aggregated by ASCO's CancerLinQ platform were analyzed. MATERIALS AND METHODS Using the CancerLinQ health technology platform, 19 Oncology MIPS (oMIPS) CQMs were evaluated to determine the presence of data elements (DEs) necessary to satisfy each CQM and the DE percent population with patient data (fill rates). At the time of this analysis, the CancerLinQ network comprised 63 active practices, representing eight different EHR vendors and containing records for more than 1.63 million unique patients with one or more malignant neoplasms (1.73 million cancer cases). RESULTS Fill rates for the 63 oMIPS-associated DEs varied widely among the practices. The average site had at least one filled DE for 52% of the DEs. Only 35% of the DEs were populated for at least one patient record in 95% of the practices. However, the average DE fill rate of all practices was 23%. No data were found at any practice for 22% of the DEs. Since any oMIPS CQM with an unpopulated DE component resulted in an inability to compute the measure, only two (10.5%) of the 19 oMIPS CQMs were computable for more than 1% of the patients. CONCLUSION Although EHR systems had relatively high DE fill rates for some DEs, underfilling and inconsistency of DEs in EHRs render automated oncology MIPS CQM calculations impractical.
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Affiliation(s)
| | | | - Jacob Koskimaki
- CancerLinQ, American Society of Clinical Oncology, Alexandria, VA
| | - Elmer V. Bernstam
- The University of Texas School of Biomedical Informatics at Houston and Division of General Internal Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, TX
| | | | - Robert S. Miller
- CancerLinQ, American Society of Clinical Oncology, Alexandria, VA
| | - James L. Chen
- Departments of Internal Medicine and Biomedical Informatics, The Ohio State University, Columbus, OH
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Torous VF, Simpson RW, Balani JP, Baras AS, Berman MA, Birdsong GG, Giannico GA, Paner GP, Pettus JR, Sessions Z, Sirintrapun SJ, Srigley JR, Spencer S. College of American Pathologists Cancer Protocols: From Optimizing Cancer Patient Care to Facilitating Interoperable Reporting and Downstream Data Use. JCO Clin Cancer Inform 2021; 5:47-55. [PMID: 33439728 PMCID: PMC8140812 DOI: 10.1200/cci.20.00104] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
The College of American Pathologists Cancer Protocols have offered guidance to pathologists for standard cancer pathology reporting for more than 35 years. The adoption of computer readable versions of these protocols by electronic health record and laboratory information system (LIS) vendors has provided a mechanism for pathologists to report within their LIS workflow, in addition to enabling standardized structured data capture and reporting to downstream consumers of these data such as the cancer surveillance community. This paper reviews the history of the Cancer Protocols and electronic Cancer Checklists, outlines the current use of these critically important cancer case reporting tools, and examines future directions, including plans to help improve the integration of the Cancer Protocols into clinical, public health, research, and other workflows.
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Affiliation(s)
| | | | - Jyoti P Balani
- University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Michael A Berman
- Jefferson Hospital, Allegheny Health Network, Jefferson Hills, PA
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Yu P, Kibbe W. Cancer Data Science and Computational Medicine. JCO Clin Cancer Inform 2021; 5:487-489. [PMID: 33950710 DOI: 10.1200/cci.21.00006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Affiliation(s)
- Peter Yu
- Hartford Healthcare Cancer Institute, Hartford, CT
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