A Roadmap for Improving Medicare's Application of Coverage With Evidence Development.

The Centers for Medicare and Medicaid Services' coverage with evidence development (CED) policy allows the agency to provide coverage for an item or service through a National Coverage Determination (NCD), conditional upon an agreement to collect evidence designed to address specific questions or uncertainties. The goals of this policy are to expedite beneficiary access to new items and services and to generate additional evidence on the impact of these items or services for Medicare beneficiaries. However, these goals have not been fully realized because of several issues with the way the policy has been implemented, including (1) a lack of clear criteria for when CED will be applied, (2) examples of CED data collection activities placing unnecessary burdens on clinicians and the potential for undue inducement on beneficiaries, and (3) a lack of clarity around the process and timeline for reconsidering and ending CED requirements. Additionally, there are cases in which the application of CED has failed to improve access to services for certain Medicare beneficiaries because no data collection activity was implemented in response to the CED requirement or because the NCD only allows the technology to be provided and studied in certain centers of excellence. We describe a roadmap for addressing these issues, which includes, for example, developing a framework to guide the application of coverage constraints in NCDs with CED requirements. Once these issues are addressed, the Centers for Medicare and Medicaid Services could consider expanding the use of CED to technologies that are not subject to NCDs.

Healthcare insurance fraud detection using data mining.

BACKGROUND: Healthcare programs and insurance initiatives play a crucial role in ensuring that people have access to medical care. There are many benefits of healthcare insurance programs but fraud in healthcare continues to be a significant challenge in the insurance industry. Healthcare insurance fraud detection faces challenges from evolving and sophisticated fraud schemes that adapt to detection methods. Analyzing extensive healthcare data is hindered by complexity, data quality issues, and the need for real-time detection, while privacy concerns and false positives pose additional hurdles. The lack of standardization in coding and limited resources further complicate efforts to address fraudulent activities effectively. METHODOLGY: In this study, a fraud detection methodology is presented that utilizes association rule mining augmented with unsupervised learning techniques to detect healthcare insurance fraud. Dataset from the Centres for Medicare and Medicaid Services (CMS) 2008-2010 DE-SynPUF is used for analysis. The proposed methodology works in two stages. First, association rule mining is used to extract frequent rules from the transactions based on patient, service and service provider features. Second, the extracted rules are passed to unsupervised classifiers, such as IF, CBLOF, ECOD, and OCSVM, to identify fraudulent activity. RESULTS: Descriptive analysis shows patterns and trends in the data revealing interesting relationship among diagnosis codes, procedure codes and the physicians. The baseline anomaly detection algorithms generated results in 902.24 seconds. Another experiment retrieved frequent rules using association rule mining with apriori algorithm combined with unsupervised techniques in 868.18 seconds. The silhouette scoring method calculated the efficacy of four different anomaly detection techniques showing CBLOF with highest score of 0.114 followed by isolation forest with the score of 0.103. The ECOD and OCSVM techniques have lower scores of 0.063 and 0.060, respectively. CONCLUSION: The proposed methodology enhances healthcare insurance fraud detection by using association rule mining for pattern discovery and unsupervised classifiers for effective anomaly detection.

Validation of Claims-Based Algorithm for Lyme Disease, Massachusetts, USA.

Compared with notifiable disease surveillance, claims-based algorithms estimate higher Lyme disease incidence, but their accuracy is unknown. We applied a previously developed Lyme disease algorithm (diagnosis code plus antimicrobial drug prescription dispensing within 30 days) to an administrative claims database in Massachusetts, USA, to identify a Lyme disease cohort during July 2000-June 2019. Clinicians reviewed and adjudicated medical charts from a cohort subset by using national surveillance case definitions. We calculated positive predictive values (PPVs). We identified 12,229 Lyme disease episodes in the claims database and reviewed and adjudicated 128 medical charts. The algorithm's PPV for confirmed, probable, or suspected cases was 93.8% (95% CI 88.1%-97.3%); the PPV was 66.4% (95% CI 57.5%-74.5%) for confirmed and probable cases only. In a high incidence setting, a claims-based algorithm identified cases with a high PPV, suggesting it can be used to assess Lyme disease burden and supplement traditional surveillance data.

Number-dependent association of potentially inappropriate medications with clinical outcomes and expenditures among community-dwelling older adults: A population-based cohort study.

AIMS: The aim of this study was to investigate the prevalence of potentially inappropriate medication (PIM) prescribing and its number-dependent association (PIM = 1, 2, ≥3) with all-cause hospitalizations, emergency department (ED) visits, and medication expenditures in Beijing, China. METHODS: A retrospective cohort analysis was conducted to analyse PIM prescribing in community-dwelling older adults aged ≥65 years within the Beijing Municipal Medical Insurance Database (data from July to September 2016). The prevalence of PIMs was estimated based on the 2015 Beers Criteria. Logistic models were utilized to investigate the associations between PIM use and all-cause hospitalizations and ED visits. Generalized linear models with the logic link and gamma distribution were used to analyse associations between PIM use and medication expenditures. RESULTS: Among the 506 214 older adults, the prevalence of PIM was 38.07%. After adjusting for covariables, prescribing two and three or more PIMs was associated with increased risks of hospitalizations (PIM = 2: odds ratio [OR] 1.34, 95% confidence interval [CI]: 1.22-1.47; PIM ≥ 3: OR = 1.47, 95% CI: 1.32-1.63) and ED visits (PIM = 2: OR = 1.29, 95% CI 1.12-1.48; PIM ≥ 3: OR = 1.23, 95% CI: 1.04-1.44). Exposures to two and three or more PIMs were associated with higher medication expenditures for inpatient visits (PIM = 2: incidence rate ratio [IRR] = 1.08, 95% CI 1.01-1.16; PIM ≥ 3: IRR = 1.18, 95% CI: 1.08-1.28). Vasodilators were the most frequent PIM prescribing group among patients who were hospitalized or had to visit the ED. CONCLUSIONS: PIMs were prescribed at a high rate among community-dwelling older adults in Beijing. Two or more PIMs were associated with increased risks of hospitalizations, ED visits, and increased inpatient medication expenditures. Effective interventions are needed to target unnecessary and inappropriate medications in older adults.

Confederated learning in healthcare: Training machine learning models using disconnected data separated by individual, data type and identity for Large-Scale health system Intelligence.

BACKGROUND: A patient's health information is generally fragmented across silos because it follows how care is delivered: multiple providers in multiple settings. Though it is technically feasible to reunite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization for this purpose. OBJECTIVES: Machine learning can be conducted in a federated manner on patient datasets with the same set of variables but separated across storage. But federated learning cannot handle the situation where different data types for a given patient are separated vertically across different organizations and when patient ID matching across different institutions is difficult. We call methods that enable machine learning model training on data separated by two or more dimensions "confederated machine learning", which we aim to develop in this study. METHODS: We propose and evaluate confederated learning for training machine learning models to stratify the risk of several diseases among silos when data are horizontally separated by individual, vertically separated by data type, and separated by identity without patient ID matching. The confederated learning method can be intuitively understood as a distributed learning method with representation learning, generative model, imputation method and data augmentation elements. RESULTS: Our confederated learning method achieves AUCROC (Area Under The Curve Receiver Operating Characteristics) of 0.787 for diabetes prediction, 0.718 for psychological disorders prediction, and 0.698 for Ischemic heart disease prediction using nationwide health insurance claims. CONCLUSION: Our proposed confederated learning method successfully trained machine learning models on health insurance data separated by two or more dimensions.

Characteristics of medicine use for children with asthma in China: a nationwide population-based study.

OBJECTIVE: To analyze the asthma medication use in Chinese children of different age groups, regions, and levels of cities in China, based on the 2015 Healthcare Insurance Data in China. METHODS: The China Healthcare Insurance Research Association (CHIRA) database was searched for children from 0 to 14 years old diagnosed as asthma based on the "J45" and "J46" coded in ICD-10. A cross-sectional study design was employed. RESULTS: A total of 308,550 children were identified, all of whom were treated under the coverage of healthcare insurance. Among them, 2,468 children were eligible for inclusion in the present study. Compared with the current status of asthma care in European and American countries, under the guidelines for the diagnosis and treatment of asthma in China, the use percentages of ICS and short-acting ß2 receptor agonist in children with asthma in China were lower, but the use percentages of oral corticosteroids, long-acting ß2 receptor agonist, and theophylline (especially intravenous theophylline) were higher, especially in the Central and West China. CONCLUSION: The asthma medication use was attributed to many factors, thus efforts are still needed to further popularize the GINA programs and China's guidelines for asthma diagnosis and treatment, especially in the Central and West China.

Predicting demand for long-term care using Japanese healthcare insurance claims data.

BACKGROUND: Driven by the rapid aging of the population, Japan introduced public long-term care insurance to reinforce healthcare services for the elderly in 2000. Precisely predicting future demand for long-term care services helps authorities to plan and manage their healthcare resources and citizens to prevent their health status deterioration. METHODS: This paper presents our novel study for developing an effective model to predict individual-level future long-term care demand using previous healthcare insurance claims data. We designed two discriminative models and subsequently trained and validated the models using three learning algorithms with medical and long-term care insurance claims and enrollment records, which were provided by 170 regional public insurers in Gifu, Japan. RESULTS: The prediction model based on multiclass classification and gradient-boosting decision tree achieved practically high accuracy (weighted average of Precision, 0.872; Recall, 0.878; and F-measure, 0.873) for up to 12 months after the previous claims. The top important feature variables were indicators of current health status (e.g., current eligibility levels and age), risk factors to worsen future healthcare status (e.g., dementia), and preventive care services for improving future healthcare status (e.g., training and rehabilitation). CONCLUSIONS: The intensive validation tests have indicated that the developed prediction method holds high robustness, even though it yields relatively lower accuracy for specific patient groups with health conditions that are hard to distinguish.

Does the separating of hospital revenue from drug sales reduce the burden on patients? Evidence from China.

BACKGROUND: Since 2015, all pilot cities of public hospital reform in China have allowed the zero-markup drug policy and implemented the policy of Separating of Hospital Revenue from Drug Sales (SHRDS). The objective of this study is to evaluate whether SHRDS policy reduces the burden on patients, and to identify the mechanism through which SHRDS policy affects healthcare expenditure. METHODS: In this study, we use large sample data of urban employee's healthcare insurance in Chengdu, and adopt the difference in difference model (DID) to estimate the impact of the SHRDS policy on total healthcare expenditures and drug expenditure of patients, and to provide empirical evidence for deepening medical and health system reform in China. RESULTS: After the SHRDS policy's implementation, the total healthcare expenditure kept growing, but the growth rate slowed down between 2014 to 2015. The total healthcare expenditure of patients decreased by only 0.6%, the actual reimbursement expenditure of patients decreased by 4.1%, the reimbursement ratio decreased by 2.6%. and the drugs expenditure dropped by 14.4%. However, the examinations expenditure increased by 18.2%, material expenditure increased significantly by 38.5%, and nursing expenditure increased by 12.7%. CONCLUSIONS: After implementing the SHRDS policy, the significant reduction in drug expenditure led to more physicians inducing patients' healthcare service needs, and the increased social healthcare burden was partially transferred to the patients' personal economic burden through the decline in the reimbursement ratio. The SHRDS policy is not an effective way to control healthcare expenditure.

Abuse detection in healthcare insurance with disease-treatment network embedding.

Abuse in healthcare insurance refers to a medical service or practice inconsistent with the generally accepted sound fiscal practices, such as overtreatment or overcharging. These types of abuses may lead to prescriptions that do not meet the criteria for medical stability. On the other hand, abuse may incur unnecessary costs by deliberately executing gratuitous treatments. In efforts to detect and prevent abuse, insurance companies hire medical professionals to manually examine the legitimacy of claim filings. It is, however, very costly in terms of labor and time to review all of the claims given the exploding amount of filings. In this light, there are growing interests for employing data mining techniques to automatically detect abusive claims or providers showing an abnormal billing pattern. Unfortunately, most of these models do not consider the disease-treatment information explicitly. In order for detection models to properly address the issues rising from individual drugs with similar efficacy, it is absolutely essential to account for the relationship between diseases and treatments during the learning process. In this paper, we propose a network-based approach which assesses the relationship between the diseases and treatments when detecting abuse from claim filings. Our proposed model consists of three stages. During the first stage, a disease-treatment network is constructed based on information extracted from the claim filings. Since the association between diseases and treatments is not explicitly expressed on these filings, we infer the disease-treatment relationship by computing the relative risk (RR). Second stage involves selecting the best graph embedding method from several candidates. We select the best method by comparing performances on link prediction. At the final stage, we solve a link prediction problem as a vehicle to detecting overtreatments. If our link prediction model predicts links to be nonexistent for all of the diseases and treatments listed in a given claim, then the claim is classified as an overtreatment case. We test the proposed model using the real-world claim data and showed that the proposed method classifies the treatment well which does not explicitly exist in the training network.

The effect of expanded insurance coverage under the Affordable Care Act on emergency department utilization in New York.

BACKGROUND: One of the proposed benefits of expanding insurance coverage under the Affordable Care Act (ACA) was a reduction in emergency department (ED) utilization for non-urgent visits related to lack of health insurance coverage and access to primary care providers. The objective of this study was to estimate the effect of the 2014 ACA implementation on ED use in New York. METHODS: We used the Healthcare Cost and Utilization Project State Emergency Department and State Inpatient Databases for all outpatient and all inpatient visits for patients admitted through an ED from 2011 to 2016. We focused on in-state residents aged 18 to 64, who were covered under Medicaid, private insurance, or were uninsured prior to the 2014 expansion. We estimated the effect of the expanded insurance coverage on average monthly ED visits volumes and visits per 1000 residents (rates) using interrupted time-series regression analyses. RESULTS: After ACA implementation, overall average monthly ED visits increased by around 3.0%, both in volume (9362; 95% Confidence Intervals [CI]: 1681-17,522) and in rates (0.80, 95% CI:0.12-1.49). Medicaid covered ED visits volume increased by 23,972 visits (95% CI: 16,240 -31,704) while ED visits by the uninsured declined by 13,297 (95% CI:-15,856 - -10,737), and by 1453 (95% CI:-4027-1121) for the privately insured. Medicaid ED visits rates per 1000 residents increased by 0.77 (95% CI:-1.96-3.51) and by 2.18 (95% CI:-0.55-4.92) for those remaining uninsured, while private insurance visits rates decreased by 0.48 (95% CI:-0.79 - -0.18). We observed increases in primary-care treatable ED visits and in visits related to mental health and alcohol disorders, substance use, diabetes, and hypertension. All estimated changes in monthly ED visits after the expansion were statistically significant, except for ED visit rates among Medicaid beneficiaries. CONCLUSION: Net ED visits by adults 18 to 64 years of age increased in New York after the implementation of the ACA. Large increases in ED use by Medicaid beneficiaries were partially offset by reductions among the uninsured and those with private coverage. Our results suggest that efforts to expand health insurance coverage only will be unlikely to reverse the increase in ED use.

A historical perspective of how public policy shaped dialysis care delivery in the United States.

Broadly defined public policy has been said to be whatever "governments choose to do or not to do" As applied to healthcare, public policy can be traced back to the 4000-year-old Code of Hammurabi. As it applies to dialysis care its history is barely 50 years old since national coverage for end-stage renal disease (ESRD) was legislated as Public Law 92-603 in 1972. As with most healthcare policy changes, it was a result of medical progress which had changed renal function replacement by dialysis from its rudimentary beginnings during the Second World War into an experimental acute life-saving procedure in the 1950s and to an established life-sustaining treatment for the otherwise fatal disease of uremia in the 1960s that was limited by its costs. Since 1973, the Medicare ESRD Program has saved the lives of thousands of individuals, a compassionate achievement that has come at increasing costs which have exceeded all estimates and evaded containment. Apart from cost containment, policy changes in dialysis care have been directed at improving its safety and adequacy. Some of the results of these changes are evident as one compares the outcomes and complications of dialysis encountered in the 1970s to those in the present; others, particularly those related to vascular access and hospitalization rates have improved modestly. This article recounts the historical background in which national coverage for dialysis care was developed, legislated and has evolved over the past 50 years.

ERAS, length of stay and private insurance: a retrospective study.

PURPOSE: Hospital discharge after colorectal resection within an Enhanced Recovery After Surgery (ERAS) program occurs earlier compared to standard-care postoperative pathways but often later than what objective criteria of "readiness for discharge" could allow. The aim of this study was to analyse reasons and risk factors of such discharge delay. METHODS: All elective patients admitted for colorectal resection at the regional Hospital of Lugano in 2014 and 2015 were included. The postoperative day on which patients fulfilled consensus agreed criteria (according to Fiore) for readiness for discharge (POD-F) and the effective day of discharge (POD-D) were determined. We analysed the reasons for discharge delay (POD-D>POD-F) and performed univariate and multivariate analysis to determine risk factors. RESULTS: One hundred thirty-eight patients were included in the study. Median POD-F was 5 (2-48) days, POD-D was 6 (3-50) days. In 94 patients, POD-D occurred later than POD-F with a median delay of 1 (1-11) days. Reasons for discharge delay were insufficient social support in 13 (14%), patient's preference in 39 (41%) and medical team preference in 41 (44%). Private insurance (OR 2.61, 95%CI 1.08-6.34, p = 0.034) and patient discharged on a day other than Monday (OR 2.94, 95%CI 1.16-7.14, p = 0.023) were independent predictors for discharge delay. CONCLUSION: Even when objective criteria for readiness for discharge have been fulfilled, patients and/or doctors often do not feel comfortable with hospital discharge at this time point. Length of stay, even within an ERAS program, is still influenced by several non-medical factors and is therefore not a precise surrogate marker of outcomes.

[Prevalence of rheumatoid arthritis in Germany based on health insurance data : Regional differences and first results of the PROCLAIR study]. / Prävalenz der rheumatoiden Arthritis in Deutschland auf Basis von Kassendaten : Regionale Unterschiede und erste Ergebnisse der PROCLAIR-Studie.

BACKGROUND: Rheumatoid arthritis (RA) is the most common chronic inflammatory joint disease with a prevalence of up to 1 % in the adult population. OBJECTIVE: This study describes the prevalence of RA diagnoses in outpatient health insurance claims data, based on different case definitions and stratified by age, sex and region of residence. METHODS: Based on data from a nationwide statutory health insurance fund (BARMER GEK) from the year 2013, a cross-sectional study of insurants aged 18 years or older was conducted. The following case definitions were applied: A) a diagnosis of seropositive rheumatoid arthritis (M05) or other rheumatoid arthritis (M06) according to the international classification of diseases 10 German modification (ICD-10-GM) in at least two quarterly periods of the year 2013, B) case definition A plus determination of C­reactive protein (CRP) or erythrocyte sedimentation rate (ESR) at least once, C) case definition B plus specific drug therapy and D) case definition A plus treatment by a rheumatologist. Raw as well as age and sex-standardized prevalences were calculated and stratified according to the federal state. RESULTS: The study population consisted of 7,155,315 insurants of whom 60.2 % were female. Overall, RA prevalences for the respective case definitions were 1.62 % (A), 1.11 % (B), 0.94 % (C) and 0.64 % (D). When standardized to the German population the prevalences were 1.38 % (A), 0.95 % (B), 0.81 % (C) and 0.55 % (D). The proportion of women was approximately 80 % for all case definitions. Prevalences increased with age, peaking in the age group 70-79 years old and showing the highest values in eastern and the lowest in southern Germany for raw as well as standardized measures. CONCLUSION: Regional differences in the prevalence of RA diagnoses in health insurance claims data were observed independent of age, sex and case definition. The expected prevalence according to the results of international studies was best achieved when case definitions with CRP or ESR were considered.

[Prognostic assessment in private healthcare insurance part 1 : Risk of osteoarthritis]. / Prognosebeurteilung in der Privaten Unfallversicherung Teil 1 : Arthroserisiko.

After examining the cause of an accident the medical expert working in the area of private health care insurance under the general accident insurance (AUB) sample conditions must ascertain incapacity within a period of time that has been contractually agreed upon between the parties involved. In addition, this person must also state their position on the question as to whether there may exist any circumstances up to the latest possible point in time in insurance terms that would comprise an adequate prognosis of a future change in the long-term condition. This requires a high probability.The sole risk of the evolution of the functional deficit arising from a proven or prognosticated post-traumatic osteoarthritis is excluded from this standard of proof which means that flat-rate risk supplements are not suited to this individualized approach and thus do not apply.

Changes in Medicare Reimbursement for Top Burn Surgery Procedures by State Between 2011 and 2022.

This study analyzed the geographic variation in annual Medicare reimbursement changes for common burn surgery procedures from 2011 to 2022 to clarify trends in reimbursement. The Center for Medicare and Medicaid Services' Physician fee schedule database was analyzed to find state-by-state reimbursement rates for the most common burn surgery procedures. Physician reimbursement was adjusted for inflation utilizing the consumer price index. Procedures were weighted according to frequency, and an inflation-adjusted percent change was identified for each state. Since 2011, the inflation-adjusted Medicare reimbursement for the top burn surgery procedures for all U.S. states decreased by a yearly average of 2.67%. Washington (-2.17%), New York (-2.31%), Oregon (-2.33%), and the District of Columbia (-2.35%) showed the smallest annual percent change. Illinois (-3.34%), Mississippi (-3.04%), Idaho (-2.99%), and Michigan (-2.96%) were the states with the greatest annual decrease. The most common procedures included initial treatment of burns (16000), burn dressing and debridement (16020, 16025, 16030), and burn eschar incision (16035). Medicare reimbursement for burn surgery procedures decreased from 2011 to 2022. The geographic variance in reimbursement patterns may incentivize physicians to pursue other surgical specialties or practice in certain areas which could limit access to care in low reimbursement areas. Further research is needed to examine disparities that may have arisen due to decreasing reimbursement over the last decade. New action is also needed to moderate diminishing burn surgery reimbursement to ensure quality care for Medicare beneficiaries in low-reimbursement states.

[Chances and risks of conversion to outpatient treatment in trauma surgery and orthopedics]. / Chancen und Risiken in der Ambulantisierung der Unfallchirurgie und Orthopädie.

At the latest since the Medical Services Healthcare Insurance Reform Act (MDK), the declared will of the legislation is the conversion of operations previously carried out in an inpatient setting to an outpatient setting. In trauma surgery and orthopedics numerous operations are carried out that could principally also be performed in an outpatient setting; however, a prerequisite is a medical assessment of the suitability of patients as well as an economic and normative framework that makes outpatient surgery attractive. Both the Outpatient Surgery in Hospitals Catalogue (AOP-Katalog) and the first edition of the Hybrid Diagnosis-related Groups (DRG) define interventions in trauma surgery that could be carried out in an outpatient setting. Hospitals are therefore required to find solutions for these interventions under processual and economic provisos. These range from omission of outpatient operations to the expansion as a separate financial department in the hospital. With the introduction of the hybrid DRG, the legislation enables equal remuneration for outpatient versus short-term inpatient treatment and leaves the case management up to the hospital; however, the performance of the AOP in the setting of a hospital and also hybrid case flat rates are as a rule not economically viable and bear the risk of the failure of all efforts at conversion to outpatient settings. It is necessary to carry out a fundamental revision of the remuneration and framework conditions for outpatient operations in trauma surgery and orthopedics in hospitals, involving practitioners. This is the only way that the conversion to outpatient treatment can succeed.

Barriers to Eye Care for Adults in the United States and Solutions for It: A Literature Review.

Vision loss and blindness is a significant public health concern that has had a profound impact on various communities in the United States. Both anticipated and unforeseen barriers have been linked to the rising rates of vision loss and blindness in the country. Extensive research has identified numerous barriers that put many Americans at a disadvantage when trying to seek high-quality eye care services. Not only do the barriers to eye care services create problems for eye health, but also create a poor quality of life. Therefore, understanding and identifying barriers to eye healthcare services is incredibly important. In addition to understanding and identifying barriers, it is also important to identify solutions to the problems created by these barriers. A systematic review of articles characterizing the barriers to eye care was completed which resulted in the identification of the major barriers that affect Americans. The review of previous research was also used to identify available solutions for problems associated with the barriers to eye care services. The major barriers identified were cost, insurance, transport and accessibility, eye health care literacy, and communication. Because of the identification of the major barriers, solutions were also identified. Health education and increased vision screenings were found to be the most used forms of solutions by healthcare professionals promoting good eye health. Telemedicine has also been cited as a possible solution to the growing problem of visual impairment and blindness within the American population.

Accessibility to Obstetric Care in South Florida Based on Insurance: A Cross-Sectional Study.

Introduction Obstetrical research confirms that earlier onset prenatal care significantly improves pregnancy and birth outcomes. Initiating care in the second trimester or having less than 50% of recommended visits has been associated with an increased risk of prematurity, stillbirth, neonatal, and infant death. Studies have shown that women on public health insurance plans initiate prenatal care substantially later into pregnancy than those on private plans. The purpose of this study is to assess whether public health insurance limits Florida patients' access to obstetric care.  Methods  A cross-sectional study was conducted by collecting data on the four most populated zip codes for Medicaid in South Florida using HealthGrades.com. The following search parameters were used: "obstetric care", "four stars and up" and "10-mile distance". Each obstetrician was called three times to assess appointment availability for fictional nulliparous women at eight weeks of gestation requesting prenatal care. Accepted insurance types (Medicaid, Cigna, and United Health Group (UHG)), time to an appointment in business days, and self-pay rates were recorded. Practices with invalid contact information and retired obstetricians were excluded. Summary statistics, chi-squared analysis, and a two-way t-test were conducted for the primary outcome.  Results  Seventy-one out of 178 obstetricians were successfully contacted, of which 31 physicians accepted all three insurances, and 40 physicians did not accept at least one insurance. Of those, 97.2% accepted UnitedHealthcare, 98.6% accepted Cigna, and 45.1% accepted Medicaid. There was a statistically significant difference when comparing acceptance rates between UHC and Medicaid as well as Cigna and Medicaid (p<0.001). There was no statistically significant difference in acceptance rates in the direct comparison of the two private insurances, Cigna and UnitedHealthcare (p=0.559). The average number of days until the next available appointment was 12.7 (SD= 7.2) for UnitedHealthcare, 20.0 (SD=6.7) for Cigna, and 17.0 (SD=8.6) for Medicaid. There was a statistically significant trend between the type of insurance and the time to the earliest appointment (p=0.002).  Conclusion  This study demonstrated patients enrolled in Medicaid in South Florida have significantly less access to prenatal care than those with private insurance. This evidence shows that decreased access to care from Medicaid plans can possibly increase the risk of adverse outcomes associated with inadequate prenatal care. This information should be considered by policymakers when considering future Medicaid expansion.

Analysis of the annual changes in dental institutions that claimed dental sedatives in Korea and the types of sedatives using health care big data.

Background: Dentists make various efforts to reduce patients' anxiety and fear associated with dental treatment. Dental sedation is an advanced method that dentists can perform to reduce patients' anxiety and fear and provide effective dental treatment. However, dental sedation is different from general dental treatment and requires separate learning, and if done incorrectly, can lead to serious complications. Therefore, sedation is performed by a limited number of dentists who have received specific training. This study aimed to investigate the proportion of dentists who practice sedation and the main sedatives they use in the context of the Republic of Korea. Methods: We used the customized health information data provided by the Korean National Health Insurance. We investigated the number of dental hospitals or clinics that claimed insurance for eight main sedatives commonly used in dental sedation from January, 2007 to September, 2019 at the Health Insurance Review and Assessment Service. We also identified the changes in the number of dental medical institutions by region and year and analyzed the number and proportion of dental medical institutions prescribing each sedative. Results: In 2007, 302 dental hospitals prescribed sedatives, and the number increased to 613 in 2019. In 2007, approximately 2.18% of the total 13,796 dental institutions prescribed sedatives, increasing to 3.31% in 2019. In 2007, 168 institutions (55.6%) prescribed N2O alone, and in 2019, 510 institutions (83.1%) made claims for it. In 2007, 76 (25.1%) hospitals made claims for chloral hydrate, but the number gradually decreased, with only 29 hospitals (4.7%) prescribing it in 2019. Hospitals that prescribed a combination of N2O, chloral hydrate, and hydroxyzine increased from 27 (8.9%) in 2007 to 51 (9%) in 2017 but decreased to 38 (6.1%) in 2019. The use of a combination of N2O and midazolam increased from 20 hospitals (6.6%) in 2007 to 51 hospitals (8.3%) in 2019. Conclusion: While there is a critical limitation to the investigation of dental hospitals performing sedation using insurance claims data, namely exclusion of dental clinics providing non-insured treatments, we found that in 2019, approximately 3.31% of the dental clinics were practicing sedation and that N2O was the most commonly prescribed sedative.

Virtual patient identifier (vPID): Improving patient traceability using anonymized identifiers in Japanese healthcare insurance claims database.

Objective: Japan's national-level healthcare insurance claims database (NDB) is a collective database that contains the entire information on healthcare services being provided to all citizens. However, existing anonymized identifiers (ID1 and ID2) have a poor capability of tracing patients' claims in the database, hindering longitudinal analyses. This study presents a virtual patient identifier (vPID), which we have developed on top of these existing identifiers, to improve the patient traceability. Methods: vPID is a new composite identifier that intensively consolidates ID1 and ID2 co-occurring in an identical claim to allow to collect claims of each patient even though its ID1 or ID2 may change due to life events or clerical errors. We conducted a verification test with prefecture-level datasets of healthcare insurance claims and enrollee history records, which allowed us to compare vPID with the ground truth, in terms of an identifiability score (indicating a capability of distinguishing a patient's claims from another patient's claims) and a traceability score (indicating a capability of collecting claims of an identical patient). Results: The verification test has clarified that vPID offers significantly higher traceability scores (0.994, Mie; 0.997, Gifu) than ID1 (0.863, Mie; 0.884, Gifu) and ID2 (0.602, Mie; 0.839, Gifu), and comparable (0.996, Mie) and lower (0.979, Gifu) identifiability scores. Discussion: vPID is seemingly useful for a wide spectrum of analytic studies unless they focus on sensitive cases to the design limitation of vPID, such as patients experiencing marriage and job change, simultaneously, and same-sex twin children. Conclusion: vPID successfully improves patient traceability, providing an opportunity for longitudinal analyses that used to be practically impossible for NDB. Further exploration is also necessary, in particular, for mitigating identification errors.