Perspectives in health information management / AHIMA, American Health Information Management Association最新文献

{"title":"Big Data Analytics in Healthcare: Investigating the Diffusion of Innovation.","authors":"Diane Dolezel,&nbsp;Alexander McLeod","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The shortage of data scientists has restricted the implementation of big data analytics in healthcare facilities. This survey study explores big data tool and technology usage, examines the gap between the supply and the demand for data scientists through Diffusion of Innovations theory, proposes engaging academics to accelerate knowledge diffusion, and recommends adoption of curriculum-building models. For this study, data were collected through a national survey of healthcare managers. Results provide practical data on big data tool and technology skills utilized in the workplace. This information is valuable for healthcare organizations, academics, and industry leaders who collaborate to implement the necessary infrastructure for content delivery and for experiential learning. It informs academics working to reengineer their curriculum to focus on big data analytics. The paper presents numerous resources that provide guidance for building knowledge. Future research directions are discussed.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"16 Summer","pages":"1a"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6669368/pdf/phim0016-0001f.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41215350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the ICD-10-CM Transition and Post-implementation Stages: A Public Health Institution Case Study.","authors":"Judith P Monestime,&nbsp;Roger W Mayer,&nbsp;Audrey Blackwood","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>On October 1, 2015, the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) was incorporated into the US public health system. Because of significant opposition and reservations expressed by stakeholders, while the proposed rule for ICD-10-CM adoption was issued in 2009, the transition did not occur until October 2015. The purpose of this study was to identify conversion initiatives used by a public health institution during the initial and subsequent stages of ICD-10-CM implementation, to help similar institutions address future unfunded healthcare data infrastructure mandates. The data collection for this study occurred from 2015 to 2018, encompassing 20 semistructured interviews with 13 department heads, managers, physicians, and coders. Research findings from this study identified several trends, disruptions, challenges, and lessons learned that might support the industry with strategies to foster success for the transition to future coding revisions (i.e., ICD-11).</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"16 Spring","pages":"1a"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6462880/pdf/phim0016-0001d.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41215349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of ICD-9-CM to ICD-10-CM Crosswalks Derived by Physician and Clinical Coder vs. Automated Methods.","authors":"J. Simeone, Xinyue Liu, T. Bhagnani, M. Reynolds, J. Collins, E. Bortnichak","doi":"10.1016/J.JVAL.2017.08.2048","DOIUrl":"https://doi.org/10.1016/J.JVAL.2017.08.2048","url":null,"abstract":"","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"18 Spring 1","pages":"1e"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/J.JVAL.2017.08.2048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43783454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the Collection of Race, Ethnicity, and Language Data to Reduce Healthcare Disparities: A Case Study from an Academic Medical Center.","authors":"Wei-Chen Lee, Sreenivas P Veeranki, Hani Serag, Karl Eschbach, Kenneth D Smith","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Well-designed electronic health records (EHRs) must integrate a variety of accurate information to support efforts to improve quality of care, particularly equity-in-care initiatives. This case study provides insight into the challenges those initiatives may face in collecting accurate race, ethnicity, and language (REAL) information in the EHR. We present the experience of an academic medical center strengthening its EHR for better collection of REAL data with funding from the EHR Incentive Programs for meaningful use of health information technology and the Texas Medicaid 1115 Waiver program. We also present a plan to address some of the challenges that arose during the course of the project. Our experience at an academic medical center can provide guidance about the likely challenges similar institutions may expect when they implement new initiatives to collect REAL data, particularly challenges regarding scope, personnel, and other resource needs.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"13 Fall","pages":"1g"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5075235/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72211130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Family History Data Mining with Neural Networks: A Pilot Study.","authors":"Robert Hoyt, Steven Linnville, Stephen Thaler, Jeffrey Moore","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Following the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, electronic health records were widely adopted by eligible physicians and hospitals in the United States. Stage 2 meaningful use menu objectives include a digital family history but no stipulation as to how that information should be used. A variety of data mining techniques now exist for these data, which include artificial neural networks (ANNs) for supervised or unsupervised machine learning. In this pilot study, we applied an ANN-based simulation to a previously reported digital family history to mine the database for trends. A graphical user interface was created to display the input of multiple conditions in the parents and output as the likelihood of diabetes, hypertension, and coronary artery disease in male and female offspring. The results of this pilot study show promise in using ANNs to data mine digital family histories for clinical and research purposes. </p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"13 ","pages":"1c"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4739442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72211127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting 30- to 120-Day Readmission Risk among Medicare Fee-for-Service Patients Using Nonmedical Workers and Mobile Technology.","authors":"Andrey Ostrovsky, Lori O'Connor, Olivia Marshall, Amanda Angelo, Kelsy Barrett, Emily Majeski, Maxwell Handrus, Jeffrey Levy","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objective: </strong>Hospital readmissions are a large source of wasteful healthcare spending, and current care transition models are too expensive to be sustainable. One way to circumvent cost-prohibitive care transition programs is complement nurse-staffed care transition programs with those staffed by less expensive nonmedical workers. A major barrier to utilizing nonmedical workers is determining the appropriate time to escalate care to a clinician with a wider scope of practice. The objective of this study is to show how mobile technology can use the observations of nonmedical workers to stratify patients on the basis of their hospital readmission risk.</p><p><strong>Materials and methods: </strong>An area agency on aging in Massachusetts implemented a quality improvement project with the aim of reducing 30-day hospital readmission rates using a modified care transition intervention supported by mobile predictive analytics technology. Proprietary readmission risk prediction algorithms were used to predict 30-, 60-, 90-, and 120-day readmission risk.</p><p><strong>Results: </strong>The risk score derived from the nonmedical workers' observations had a significant association with 30-day readmission rate with an odds ratio (OR) of 1.12 (95 percent confidence interval [CI], 1 .09-1.15) compared to an OR of 1.25 (95 percent CI, 1.19-1.32) for the risk score using nurse observations. Risk scores using nurse interpretation of nonmedical workers' observations show that patients in the high-risk category had significantly higher readmission rates than patients in the baseline-risk and mild-risk categories at 30, 60, 90, and 120 days after discharge. Of the 1,064 elevated-risk alerts that were triaged, 1,049 (98.6 percent) involved the nurse care manager, 804 (75.6 percent) involved the patient, 768 (72.2 percent) involved the health coach, 461 (43.3 percent) involved skilled nursing, and 235 (22.1 percent) involved the outpatient physician in the coordination of care in response to the alert.</p><p><strong>Discussion: </strong>The predictive nature of the 30-day readmission risk scores is influenced by both nurse and nonmedical worker input, and both are required to adequately triage the needs of the patient.</p><p><strong>Conclusion: </strong>Although this preliminary study is limited by a modest effect size, it demonstrates one approach to using technology to contribute to delivery model innovation that could curb wasteful healthcare spending by tapping into an existing underutilized workforce.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"13 ","pages":"1e"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4739444/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72211129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electronic Health Record Use a Bitter Pill for Many Physicians.","authors":"Stephen L Meigs, Michael Solomon","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Electronic health record (EHR) adoption among office-based physician practices in the United States has increased significantly in the past decade. However, the challenges of using EHRs have resulted in growing dissatisfaction with the systems among many of these physicians. The purpose of this qualitative multiple-case study was to increase understanding of physician perceptions regarding the value of using EHR technology. Important findings included the belief among physicians that EHR systems need to be more user-friendly and adaptable to individual clinic workflow preferences, physician beliefs that lack of interoperability among EHRs is a major barrier to meaningful use of the systems, and physician beliefs that EHR use does not improve the quality of care provided to patients. These findings suggest that although government initiatives to encourage EHR adoption among office-based physician practices have produced positive results, additional support may be required in the future to maintain this momentum. </p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"13 ","pages":"1d"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4739443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72211128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physicians' and Nurses' Opinions about the Impact of a Computerized Provider Order Entry System on Their Workflow.","authors":"Haleh Ayatollahi, Masoud Roozbehi, Hamid Haghani","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>In clinical practices, the use of information technology, especially computerized provider order entry (CPOE) systems, has been found to be an effective strategy to improve patient care. This study aimed to compare physicians' and nurses' views about the impact of CPOE on their workflow.</p><p><strong>Methods: </strong>This case study was conducted in 2012. The potential participants included all physicians (n = 28) and nurses (n = 145) who worked in a teaching hospital. Data were collected using a five-point Likert-scale questionnaire and were analyzed using SPSS version 18.0.</p><p><strong>Results: </strong>The results showed a significant difference between physicians' and nurses' views about the impact of the system on interorganizational workflow (p = .001) and working relationships between physicians and nurses (p = .017).</p><p><strong>Conclusion: </strong>Interorganizational workflow and working relationships between care providers are important issues that require more attention. Before a CPOE system is designed, it is necessary to identify workflow patterns and hidden structures to avoid compromising quality of care and patient safety.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"12 ","pages":"1g"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4632876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141072185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physicians' Outlook on ICD-10-CM/PCS and Its Effect on Their Practice.","authors":"Valerie Watzlaf, Zahraa Alkarwi, Sandy Meyers, Patty Sheridan","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>The United States is one of the last countries to change from ICD-9-CM to ICD-10-CM/PCS. The compliance date for implementation of ICD-10-CM/PCS is expected to fall on October 1, 2015.</p><p><strong>Objectives: </strong>Evaluate physicians' perceptions on the change from ICD-9-CM to ICD-10-CM/PCS and its effect on their practice, determine how HIM professionals can assist in this transition, and assess what resources are needed to aid in the transition.</p><p><strong>Results: </strong>Twenty physicians were asked to participate in one of three focus groups. Twelve physicians (60 percent) agreed to participate. Top concerns included electronic health record software readiness, increase in documentation specificity and time, ability of healthcare professionals to learn a new language, and inadequacy of current training methods and content.</p><p><strong>Conclusion: </strong>Physicians expressed that advantages of ICD-10-CM/PCS were effective data analytics and complexity of patient cases with more specific codes. Health information management professionals were touted as needed during the transition to create simple, clear specialty guides and crosswalks as well as education and training tools specific for physicians.</p>","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":"12 ","pages":"1b"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4700867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140194738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploration of ICD-9-CM coding of chronic disease within the Elixhauser Comorbidity Measure in patients with chronic heart failure.","authors":"Jennifer Hornung Garvin,&nbsp;Andrew Redd,&nbsp;Dan Bolton,&nbsp;Pauline Graham,&nbsp;Dominic Roche,&nbsp;Peter Groeneveld,&nbsp;Molly Leecaster,&nbsp;Shuying Shen,&nbsp;Mark G Weiner","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes capture comorbidities that can be used to risk adjust nonrandom patient groups. We explored the accuracy of capturing comorbidities associated with one risk adjustment method, the Elixhauser Comorbidity Measure (ECM), in patients with chronic heart failure (CHF) at one Veterans Affairs (VA) medical center. We explored potential reasons for the differences found between the original codes assigned and conditions found through retrospective review.</p><p><strong>Methods: </strong>This descriptive, retrospective study used a cohort of patients discharged with a principal diagnosis coded as CHF from one VA medical center in 2003. One admission per patient was used in the study; with multiple admissions, only the first admission was analyzed. We compared the assignment of original codes assigned to conditions found in a retrospective, manual review of the medical record conducted by an investigator with coding expertise as well as by physicians. Members of the team experienced with assigning ICD-9-CM codes and VA coding processes developed themes related to systemic reasons why chronic conditions were not coded in VA records using applied thematic techniques.</p><p><strong>Results: </strong>In the 181-patient cohort, 388 comorbid conditions were identified; 305 of these were chronic conditions, originally coded at the time of discharge with an average of 1.7 comorbidities related to the ECM per patient. The review by an investigator with coding expertise revealed a total of 937 comorbidities resulting in 618 chronic comorbid conditions with an average of 3.4 per patient; physician review found 872 total comorbidities with 562 chronic conditions (average 3.1 per patient). The agreement between the original and the retrospective coding review was 88 percent. The kappa statistic for the original and the retrospective coding review was 0.375 with a 95 percent confidence interval (CI) of 0.352 to 0.398. The kappa statistic for the retrospective coding review and physician review was 0.849 (CI, 0.823-0.875). The kappa statistic for the original coding and the physician review was 0.340 (CI, 0.316-0.364). Several systemic factors were identified, including familiarity with inpatient VA and non-VA guidelines, the quality of documentation, and operational requirements to complete the coding process within short time frames and to identify the reasons for movement within a given facility.</p><p><strong>Conclusion: </strong>Comorbidities within the ECM representing chronic conditions were significantly underrepresented in the original code assignment. Contributing factors potentially include prioritization of codes related to acute conditions over chronic conditions; coders' professional training, educational level, and experience; and the limited number of codes allowed in initial coding software. This study highlights the need to e","PeriodicalId":40052,"journal":{"name":"Perspectives in health information management / AHIMA, American Health Information Management Association","volume":" ","pages":"1b"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797549/pdf/phim0010-0001b.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40266690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}