Health care Affordability and Associated Concerns Among Adults Aged 65 and above in Florida

Objective: This study assessed affordability of care in a diverse sample of Floridians aged ≥ 65 to ascertain concerns about health care costs. Methods: We surveyed 170 adults (40.6% white, 27.6% black, and 31.8% Hispanic) and conducted three race/ethnic-stratified focus groups ( n = 27). Results: Most participants had Medicare (97.1%). Among whites, 11.6% reported problems paying medical bills in the past 12 months versus 14.9% of blacks and 24.1% of Hispanics. In addition, 13% of whites, 19.2% of blacks, and 20.4% of Hispanics reported not getting needed prescription drugs because of costs. The most frequently identified concerns from the focus groups were the cost of prescription drugs, out-of-pocket expenses, and medical billing. Concerns about medical billing included understanding bills, transparency, timely postings, and uncertainty about who to contact about problems. Discussion: Our findings suggest that practices that help older adults effectively manage medical bills and costs may alleviate their concerns and guard against financial burdens.

Educational needs of medical practitioners about medical billing: a scoping review of the literature

Introduction The World Health Organization has suggested the solution to health system waste caused by incorrect billing and fraud is policing and prosecution. However, a growing body of evidence suggests leakage may not always be fraudulent or corrupt, with researchers suggesting medical practitioners may sometimes struggle to understand increasingly complex legal requirements around health financing and billing transactions, which may be improved through education. To explore this phenomenon further, we undertook a scoping review of the literature to identify the medical billing education needs of medical practitioners and whether those needs are being met. Methods Eligible records included English language materials published between 1 January 2000 and 4 May 2020. Searches were conducted on MEDLINE, PubMed, Google Scholar, CINAHL, LexisNexis and Heinonline. Results We identified 74 records as directly relevant to the search criteria. Despite undertaking a comprehensive, English language search, with no country restrictions, studies meeting the inclusion criteria were limited to three countries (Australia, Canada, US), indicating a need for further work internationally. The literature suggests the education needs of medical practitioners in relation to medical billing compliance are not being met and medical practitioners desire more education on this topic. Evidence suggests education may be effective in improving medical billing compliance and reducing waste in health systems. There is broad agreement amongst medical education stakeholders in multiple jurisdictions that medical billing should be viewed as a core competency of medical education, though there is an apparent inertia to include this competency in medical education curricula. Penalties for non-compliant medical billing are serious and medical practitioners are at risk of random audits and investigations for breaches of sometimes incomprehensible, and highly interpretive regulations they may never have been taught. Conclusion Despite acknowledged significance of waste in health systems due to poor practitioner knowledge of billing practices, there has been very little research to date on education interventions to improve health system efficiency at a practitioner level.

EDUCATING MEDICAL PRACTITIONERS ABOUT MEDICAL BILLING: A SYSTEMATIC REVIEW OF THE LITERATURE

ABSTRACTIntroductionThe WHO has suggested the solution to leakage in health systems caused by waste, corruption and fraud is policing and prosecution. However, a growing body of evidence suggests leakage may not always be fraudulent or corrupt, with researchers suggesting medical practitioners may sometimes struggle to understand increasingly complex legal requirements around health financing and billing transactions, which may be improved through education. To explore this phenomenon further, we undertook a systematic review of the literature to identify the medical billing education needs of medical practitioners and whether those needs are being met.MethodsEligible records included English language materials published between 1 January 2000 and 4 May 2020, including empirical research, commentary, opinions and grey literature.ResultsWe identified 74 records as directly relevant to the search criteria. Despite a comprehensive international search, studies were limited to three countries (Australia, Canada, U.S), indicating a need for further work internationally. The literature suggests the education needs of medical practitioners in relation to medical billing compliance are not being met and medical practitioners desire more education on this topic. Evidence suggests education may be effective in improving medical billing compliance and reducing waste in health systems and there is broad agreement amongst medical education stakeholders in multiple jurisdictions that medical billing should be viewed as a core competency of medical education, though there is an apparent inertia to act. Penalties for non-compliant medical billing are serious and medical practitioners are at risk of random audits and investigations for breaches of sometimes incomprehensible, and highly interpretive regulations they may never have been taught.ConclusionDespite acknowledged significance of leakage in health systems due to poor practitioner knowledge of billing practices, there has been very little research to date on education interventions to improve health system efficiency at a practitioner level.

Healthcare Affordability and Associated Concerns Among Older Adults in Florida

Rising healthcare costs create significant financial burden for Americans and is a threat to the well-being of our growing, racially/ethnically diverse, older population. In a mixed method study, we assessed ability to afford care and ascertain concerns about healthcare cost in a racially diverse sample of Floridians ages ≥ 65. We surveyed 170 adults (40.4% White, 27.6% African Americans/Black and 31.8% Latino/Hispanic) and conducted three race-stratified focus groups (n=27). Most participants had Medicare coverage (97.1%) and 27% also had Medicaid. Approximately 11.6% of Whites had problems paying medical bills in the past 12 months versus 14.9% of African Americans/Blacks and 24.1% of Latino/Hispanics. Additionally, 13% of Whites, 19.2% of African Americans/Blacks and 20.4% of Hispanics reported not getting needed prescription drugs because they could not afford them. Approximately 45.7% either perceived that their doctor “never” takes into account costs for treatment or did not know whether costs were considered. Multiple regression analyses showed no statistically significant racial/ethnic differences in affordability problems. From the focus groups, healthcare cost concerns most frequently identified by participants were the high cost of prescriptions drug, rising co-pays and out of pocket expenses, and medical billing. Participants’ concerns about medical billing included understanding their bills, transparency in billing, timely posting of charges, and uncertainty about who to talk to about billing problems. Our findings suggest that routine discussions about healthcare costs with doctors or designated healthcare personnel should help ease financial burden and healthcare costs concerns among older adults.

Neural Machine Translation Based Automatic CPT Classification System Using Surgical Procedure Text: Development and Validation Study (Preprint)

BACKGROUND Administrative costs for billing and insurance-related activities in the U.S. are substantial. One critical cause of the high overhead of administrative costs is medical billing errors. With advanced deep learning techniques, developing advanced models to predict hospital and professional billing codes becomes feasible. These models could be used for administrative cost reduction and billing process improvements. OBJECTIVE We have developed an automated anesthesiology Current Procedural Terminology (CPT) prediction system that translates manually entered surgical procedure text into standard forms using neural machine translation (NMT) techniques. The standard forms are calculated into similarity scores to predict the most appropriate CPT codes. While this system aims to enhance medical billing coding accuracy to reduce administrative costs, we compare its performance to that of machine learning algorithms previously developed. METHODS We collected and analyzed all operative procedures performed at Michigan Medicine between January 2017 and June 2019 (2.5 years). The first two years of data were used to train and validate existing models and compare the results from the NMT-based model. Data from the year 2019 (six-month follow-up period) was then used to measure the accuracy of CPT code prediction. Three experimental settings were designed with different data types to evaluate models. Experiment 1 uses the surgical procedure text entered manually in EHR. Experiment 2 uses preprocessing of the procedure text. Experiment 3 uses preprocessing of combined procedure text and preoperative diagnoses. The NMT-based model is compared with the SVM and LSTM models. RESULTS The NMT-based CPT prediction model compares favorably with SVM and LSTM models. The NMT model yielded the highest Top-1 accuracy on Experiment 1 and Experiment 2 at 81.64% and 81.71%, compared to the SVM model (81.19% and 81.27%) and the LSTM model (80.96% and 81.07%). The SVM model yielded the highest Top-1 accuracy at 84.30% on Experiment 3, followed by LSTM (83.70%) and NMT (82.80%). This Experiment 3 adding preoperative diagnoses shows 3.7%, 3.2%, and 1.3% increase of Top-1 accuracy over SVM, LSTM, and NMT in Experiment 2. For Top-3 accuracy, the SVM, LSTM, and NMT models achieved 95.64%, 95.72%, and 95.60% for Experiment 1, 95.75%, 95.67%, and 95.69% for Experiment 2, and 95.88%, 95.93%, and 95.06% for Experiment 3. CONCLUSIONS This study demonstrates the feasibility of creating an automated anesthesiology CPT classification system based on NMT techniques using surgical procedure text and preoperative diagnosis. Our results show that the performance of the NMT-based CPT prediction system is equivalent to SVM and LSTM prediction models. Importantly, we found that including preoperative diagnoses improved accuracy over using procedure text alone. CLINICALTRIAL Not applicable