scholarly journals A Decision Support System with Artificial Intelligence and Natural Language Processing to Mitigate the Deduction Rate of Health Insurance Claims

2021 ◽  
Vol 11 (24) ◽  
pp. 11623
Author(s):  
Shey-Chiang Su ◽  
Chun-Che Huang ◽  
Roger R. Gung ◽  
Li-Kai Hsiung ◽  
Zhi-Wei Gao ◽  
...  
Keyword(s):  
Health Insurance ◽  
Natural Language ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Big Data Analytics ◽  
Insurance Claims ◽  
Medical Resources ◽  
Health Insurance Claims ◽  
Based Medicine ◽  
Medical Examinations

Globally, 20% to 40% of medical resources are wasted, which could be avoided through professional audit of health insurance claims. The professional audit can pinpoint excessive use of unnecessary medicines and medical examinations. Taiwan’s National Health Insurance Bureau (TNHIB) deducts the weight that medical resources carry if regarded as unnecessary or abused when examining health insurance claims. The ratio of the deducted weight to the total weight claimed by a hospital is defined as the health insurance claim deduction rate (HICDR). A high HICDR increases the operating expenses of the hospital. In addition, it takes the hospital many resources to prepare and file appeals for the deduction. This study aims to: (1) minimize the weight deducted by the TNHIB for a hospital; and (2) facilitate efficient appeals to claim denials. It is expected that HICDR will be reduced through big data analytics. In this study, evidence-based medicine (EBM) is involved to clarify the debate, dilemmas, conflicts of interests in examining health insurance claims. A natural language method—latent Dirichlet allocation (LDA), was used to analyze patients’ medical records. The topics derived from the LDA are used as factors in the logistic regression model to estimate the probability of each claim to be deducted. The experimental results on various medical departments show that the proposed predictive model can produce accurate results, and lead to more than 41.7% reduction to the deduction of the health insurance claims. It is equivalent to more than a 750 thousand NT dollars saving per year. The efficiency of application is validated compared to the manual process that is time-consuming and labor intensive. Moreover, it is expected that this study will supplement the insufficiency of traditional methods and propose a new and effective solution to reduce the deduction rate.

scholarly journals Incorporating Unstructured Patient Narratives and Health Insurance Claims Data in Pharmacovigilance: Natural Language Processing Analysis of Patient-Generated Texts About Systemic Lupus Erythematosus

10.2196/29238 ◽  
2021 ◽  
Vol 7 (6) ◽  
pp. e29238
Author(s):  
Shinichi Matsuda ◽  
Takumi Ohtomo ◽  
Shiho Tomizawa ◽  
Yuki Miyano ◽  
Miwako Mogi ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Health Insurance ◽  
Language Processing ◽  
Lupus Erythematosus ◽  
Insurance Claims ◽  
Systemic Lupus ◽  
Patient Level ◽  
Insurance Claims Data ◽  
Health Insurance Claims Data ◽  
Health Insurance Claims

Background Gaining insights that cannot be obtained from health care databases from patients has become an important topic in pharmacovigilance. Objective Our objective was to demonstrate a use case, in which patient-generated data were incorporated in pharmacovigilance, to understand the epidemiology and burden of illness in Japanese patients with systemic lupus erythematosus. Methods We used data on systemic lupus erythematosus, an autoimmune disease that substantially impairs quality of life, from 2 independent data sets. To understand the disease’s epidemiology, we analyzed a Japanese health insurance claims database. To understand the disease’s burden, we analyzed text data collected from Japanese disease blogs (tōbyōki) written by patients with systemic lupus erythematosus. Natural language processing was applied to these texts to identify frequent patient-level complaints, and term frequency–inverse document frequency was used to explore patient burden during treatment. We explored health-related quality of life based on patient descriptions. Results We analyzed data from 4694 and 635 patients with systemic lupus erythematosus in the health insurance claims database and tōbyōki blogs, respectively. Based on health insurance claims data, the prevalence of systemic lupus erythematosus is 107.70 per 100,000 persons. Tōbyōki text data analysis showed that pain-related words (eg, pain, severe pain, arthralgia) became more important after starting treatment. We also found an increase in patients’ references to mobility and self-care over time, which indicated increased attention to physical disability due to disease progression. Conclusions A classical medical database represents only a part of a patient's entire treatment experience, and analysis using solely such a database cannot represent patient-level symptoms or patient concerns about treatments. This study showed that analysis of tōbyōki blogs can provide added information on patient-level details, advancing patient-centric pharmacovigilance.

scholarly journals Incorporating Unstructured Patient Narratives and Health Insurance Claims Data in Pharmacovigilance: Natural Language Processing Analysis of Patient-Generated Texts About Systemic Lupus Erythematosus (Preprint)

2021 ◽  
Author(s):  
Shinichi Matsuda ◽  
Takumi Ohtomo ◽  
Shiho Tomizawa ◽  
Yuki Miyano ◽  
Miwako Mogi ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Health Insurance ◽  
Language Processing ◽  
Lupus Erythematosus ◽  
Insurance Claims ◽  
Systemic Lupus ◽  
Patient Level ◽  
Insurance Claims Data ◽  
Health Insurance Claims Data ◽  
Health Insurance Claims

BACKGROUND Gaining insights that cannot be obtained from health care databases from patients has become an important topic in pharmacovigilance. OBJECTIVE Our objective was to demonstrate a use case, in which patient-generated data were incorporated in pharmacovigilance, to understand the epidemiology and burden of illness in Japanese patients with systemic lupus erythematosus. METHODS We used data on systemic lupus erythematosus, an autoimmune disease that substantially impairs quality of life, from 2 independent data sets. To understand the disease’s epidemiology, we analyzed a Japanese health insurance claims database. To understand the disease’s burden, we analyzed text data collected from Japanese disease blogs (tōbyōki) written by patients with systemic lupus erythematosus. Natural language processing was applied to these texts to identify frequent patient-level complaints, and term frequency–inverse document frequency was used to explore patient burden during treatment. We explored health-related quality of life based on patient descriptions. RESULTS We analyzed data from 4694 and 635 patients with systemic lupus erythematosus in the health insurance claims database and tōbyōki blogs, respectively. Based on health insurance claims data, the prevalence of systemic lupus erythematosus is 107.70 per 100,000 persons. Tōbyōki text data analysis showed that pain-related words (eg, pain, severe pain, arthralgia) became more important after starting treatment. We also found an increase in patients’ references to mobility and self-care over time, which indicated increased attention to physical disability due to disease progression. CONCLUSIONS A classical medical database represents only a part of a patient's entire treatment experience, and analysis using solely such a database cannot represent patient-level symptoms or patient concerns about treatments. This study showed that analysis of tōbyōki blogs can provide added information on patient-level details, advancing patient-centric pharmacovigilance.

scholarly journals Serum uric acid control for prevention of gout flare in patients with asymptomatic hyperuricaemia: a retrospective cohort study of health insurance claims and medical check-up data in Japan

2021 ◽  
pp. annrheumdis-2021-220439
Author(s):  
Ruriko Koto ◽  
Akihiro Nakajima ◽  
Hideki Horiuchi ◽  
Hisashi Yamanaka
Keyword(s):  
Uric Acid ◽  
Cohort Study ◽  
Health Insurance ◽  
Serum Uric Acid ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Disease Status ◽  
Insurance Claims ◽  
Health Insurance Claims ◽  
Gout Flare

ObjectivesIn patients with gout, treating to target serum uric acid levels (sUA) of ≤6.0 mg/dL is universally recommended to prevent gout flare. However, there is no consensus on asymptomatic hyperuricaemia. Using Japanese health insurance claims data, we explored potential benefits of sUA control for preventing gout flare in subjects with asymptomatic hyperuricaemia.MethodsThis retrospective cohort study analysed the JMDC Claims Database from April 2012 through June 2019. Subjects with sUA ≥8.0 mg/dL were identified, and disease status (prescriptions for urate-lowering therapy (ULT), occurrence of gout flare, sUA) was investigated for 1 year. Time to first onset and incidence rate of gout flare were determined by disease status subgroups for 2 years or more. The relationship between gout flare and sUA control was assessed using multivariable analysis.ResultsThe analysis population was 19 261 subjects who met eligibility criteria. We found fewer occurrences of gout flare, for both gout and asymptomatic hyperuricaemia, in patients who achieved sUA ≤6.0 mg/dL with ULT than in patients whose sUA remained >6.0 mg/dL or who were not receiving ULT. In particular, analysis by a Cox proportional-hazard model for time to first gout flare indicated that the HR was lowest, at 0.45 (95% CI 0.27 to 0.76), in subjects with asymptomatic hyperuricaemia on ULT (5.0<sUA ≤ 6.0 mg/dL), compared with untreated subjects (sUA ≥8.0 mg/dL).ConclusionsOccurrences of gout flare were reduced by controlling sUA at ≤6.0 mg/dL in subjects with asymptomatic hyperuricaemia as well as in those with gout.Trial registration numberUMIN000039985.

scholarly journals Prevalence of gout and asymptomatic hyperuricemia in the pediatric population: a cross-sectional study of a Japanese health insurance database

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Shuichi Ito ◽  
Tomoko Torii ◽  
Akihiro Nakajima ◽  
Takeshi Iijima ◽  
Hiroshi Murano ◽  
...  
Keyword(s):  
Uric Acid ◽  
Health Insurance ◽  
Pediatric Population ◽  
Gouty Arthritis ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Insurance Claims ◽  
Cross Sectional ◽  
Total Prevalence ◽  
Health Insurance Claims

Abstract Background Although gout is rare in children, chronic sustained hyperuricemia can lead to monosodium urate deposits progressing to gout, just as in adults. This study assessed prevalence and characteristics of gout and asymptomatic hyperuricemia, and incidence of gouty arthritis in the pediatric population, using data from Japanese health insurance claims. The diagnosis and treatment of pediatric gout and hyperuricemia were analyzed, and specific characteristics of those patients were assessed. Since Japanese guidelines recommend treatment with uric acid lowering drugs for asymptomatic hyperuricemia as well as for gout, these data were also used to investigate the real-world use of uric acid lowering drugs in a pediatric population. Methods This cross-sectional study was based on a 2016–2017 Japanese health insurance claims database, one of the largest epidemiology claims databases available in Japan, which included 356,790 males and 339,487 females 0–18 years of age. Outcomes were measured for prevalence, patient characteristics, treatment with uric acid lowering drugs for gout and asymptomatic hyperuricemia, and prevalence and incidence of gouty arthritis. Because uric acid can be elevated by some forms of chemotherapy, data from patients under treatment for malignancies were excluded from consideration. Results Total prevalence of gout and asymptomatic hyperuricemia in 0–18 year-olds was 0.040% (276/696,277 patients), with gout prevalence at 0.007% (48/696,277) and asymptomatic hyperuricemia at 0.033% (228/696,277). Prevalence of gout and asymptomatic hyperuricemia was highest in adolescent males, at 0.135% (176/130,823). The most common comorbidities for gout and asymptomatic hyperuricemia were metabolic syndrome at 42.8% (118/276) and kidney disease at 34.8% (96/276). Of the patients diagnosed with gout or asymptomatic hyperuricemia, 35.1% (97/276) were treated with uric acid lowering drugs. Gouty arthritis developed in 43.8% (21/48) of gout patients during the study, at an incidence of 0.65 flares/person-year. Conclusions Even the pediatric population could be affected by asymptomatic hyperuricemia, gout, and gouty arthritis, and uric acid lowering drugs are being used in this population even though those drugs have not been approved for pediatric indications. Such off-label use may indicate a potential need for therapeutic agents in this population. Trial registration UMIN000036029.

Asymmetric effect of feature level sentiment on product rating: an application of bigram natural language processing (NLP) analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yun Kyung Oh ◽  
Jisu Yi
Keyword(s):  
Natural Language Processing ◽  
Customer Satisfaction ◽  
Natural Language ◽  
Language Processing ◽  
Big Data Analytics ◽  
Service Improvement ◽  
Asymmetric Effect ◽  
Content Type ◽  
Quality Dimensions ◽  
Product Ratings

PurposeThe evaluation of perceived attribute performance reflected in online consumer reviews (OCRs) is critical in gaining timely marketing insights. This study proposed a text mining approach to measure consumer sentiments at the feature level and their asymmetric impacts on overall product ratings.Design/methodology/approachThis study employed 49,130 OCRs generated for 14 wireless earbud products on Amazon.com. Word combinations of the major quality dimensions and related sentiment words were identified using bigram natural language processing (NLP) analysis. This study combined sentiment dictionaries and feature-related bigrams and measured feature level sentiment scores in a review. Furthermore, the authors examined the effect of feature level sentiment on product ratings.FindingsThe results indicate that customer sentiment for product features measured from text reviews significantly and asymmetrically affects the overall rating. Building upon the three-factor theory of customer satisfaction, the key quality dimensions of wireless earbuds are categorized into basic, excitement and performance factors.Originality/valueThis study provides a novel approach to assess customer feature level evaluation of a product and its impact on customer satisfaction based on big data analytics. By applying the suggested methodology, marketing managers can gain in-depth insights into consumer needs and reflect this knowledge in their future product or service improvement.

scholarly journals Death at no cost? Persons with no health insurance claims in the last year of life in Switzerland

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Radoslaw Panczak ◽  
Viktor von Wyl ◽  
Oliver Reich ◽  
Xhyljeta Luta ◽  
Maud Maessen ◽  
...  
Keyword(s):  
Health Insurance ◽  
Insurance Claims ◽  
Health Insurance Claims
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