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.

A scoring model to detect abusive medical institutions based on patient classification system: Diagnosis-related group and ambulatory patient group.

The detection of medical abuse is essential because medical abuse imposes extra payments on individual insurance fees and increases unnecessary social costs. To reduce the costs due to medical abuse, insurance companies hire medical experts who examine claims, suspected to arise as a result of overtreatment from institutions, and review the suitability of claimed treatments. Owing to the limited number of reviewers and mounting volume of claims, there is need for a comprehensive method to detect medical abuse that uses a scoring model that selects a few institutions to be investigated. Numerous studies for detecting medical abuse have focused on institution-level variables such as the average values of hospitalization period and medical expenses to find the abuse score and selected institutions based on it. However, these studies use simple variables to construct a model that has poor performance with regard to detecting complex abuse billing patterns. Institution-level variables could easily represent the characteristics of institutions but loss of information is inevitable. Hence, it is possible to reduce information loss by using the finest granularity of data with treatment-level variables. In this study, we develop a scoring model by using treatment-level information and it is first of its kind to use a patient classification system (PCS) to improve the detection performance of medical abuse. PCS is a system that classifies patients in terms of clinical significance and consumption of medical resources. Because PCS is based on diagnosis, the patients grouped according to PCS tend to suffer from similar diseases. Claim data segmented by PCS is composed of patients with fewer types of diseases; hence, the data distribution by PCS is more homogeneous than data classified with respect to medical departments. We define an abusive institution as an institution having numerous number of abused treatments and containing their large sum of the abuse amounts, and the main idea of our model is that the abuse score of an institution is approximated as the sum of abuse scores for all treatments claimed from the institution. The proposed method consists of two steps: training a binary classification model to predict the abusiveness of each treatment and yielding an abuse score for each institution by aggregating the predicted abusiveness. The resulting abuse score is used to prioritize institutions to investigate. We tested the performance of our model against the scoring model employed by the insurance review agency in South Korea, making use of the real world claim data submitted to the agency. We compared these models with efficiency which represents the extent to which the model may detect the abused amounts per treatment. Experimental results show that the proposed model has efficiency up to 3.57 times higher than the model employed by the agency. In addition, we put forward an efficient and realistic reviewing process when the proposed scoring model is applied to the existing process. The proposed process has efficiency up to 2.17 times higher than the existing process.

A medical treatment based scoring model to detect abusive institutions.

Medical abuse refers to a type of abnormal medical practice which is not in compliance with qualitative or ethical standards, such as excessive prescription or overbilling of medical services. Detection of such medical abuses is crucial, especially for the patients and insurance providers, because they become subject to the extra payments incurred. As a result, insurance providers hire medical experts in order to review claims manually, yet through examination is almost impossible due to the volume of the claims filed. A typical approach is to select institutions on suspicion of abusive practices and to manually review all claims involving suspect institutions. In this light, several studies have developed models designed to extract institution-level variables. However, since these variables are at an institution-level, the model cannot account for different types of abuse practiced by individual institutions, hence degrading the accuracy of the prediction model. At the same time, these variables contain information too simple to construct an effective scoring model. In this study, we propose a model that scores the degree of abuse practiced by institutions at the treatment-level, which is the lowest level of data that can be obtained from a medical claim. Our model is the first to use such fine-grained information to construct a model for scoring the abuse by medical institutions. The proposed model consists of two stages: Training a deep neural network with embedding layers for categorical variables, and scoring the abuse degree for each treatment with the model. Then, we aggregate the resulting abuse score of each treatment and the claim amount associated with each treatment by an institution which we define as the abuse score of the institution. We test our model using real-world claim data submitted to the Health Insurance Review and Assessment (HIRA) in 2016. We also compare the performance of the proposed model against the scoring model HIRA has been using, which computes the abuse score of an institution by using institution-level variables as proposed in past literature. Experiment results show that the proposed model represents the degree of medical abuse better. In addition, the results suggest that the reviewers may examine through the claims by at most 6.1 times more efficiently than when using the scoring model with institution-level variables.

Accurate whole-genome sequencing and haplotyping from 10 to 20 human cells.

Recent advances in whole-genome sequencing have brought the vision of personal genomics and genomic medicine closer to reality. However, current methods lack clinical accuracy and the ability to describe the context (haplotypes) in which genome variants co-occur in a cost-effective manner. Here we describe a low-cost DNA sequencing and haplotyping process, long fragment read (LFR) technology, which is similar to sequencing long single DNA molecules without cloning or separation of metaphase chromosomes. In this study, ten LFR libraries were made using only ∼100 picograms of human DNA per sample. Up to 97% of the heterozygous single nucleotide variants were assembled into long haplotype contigs. Removal of false positive single nucleotide variants not phased by multiple LFR haplotypes resulted in a final genome error rate of 1 in 10 megabases. Cost-effective and accurate genome sequencing and haplotyping from 10-20 human cells, as demonstrated here, will enable comprehensive genetic studies and diverse clinical applications.

Somatic coding mutations in human induced pluripotent stem cells.

Defined transcription factors can induce epigenetic reprogramming of adult mammalian cells into induced pluripotent stem cells. Although DNA factors are integrated during some reprogramming methods, it is unknown whether the genome remains unchanged at the single nucleotide level. Here we show that 22 human induced pluripotent stem (hiPS) cell lines reprogrammed using five different methods each contained an average of five protein-coding point mutations in the regions sampled (an estimated six protein-coding point mutations per exome). The majority of these mutations were non-synonymous, nonsense or splice variants, and were enriched in genes mutated or having causative effects in cancers. At least half of these reprogramming-associated mutations pre-existed in fibroblast progenitors at low frequencies, whereas the rest occurred during or after reprogramming. Thus, hiPS cells acquire genetic modifications in addition to epigenetic modifications. Extensive genetic screening should become a standard procedure to ensure hiPS cell safety before clinical use.

High-resolution antibody dynamics of vaccine-induced immune responses.

The adaptive immune system confers protection by generating a diverse repertoire of antibody receptors that are rapidly expanded and contracted in response to specific targets. Next-generation DNA sequencing now provides the opportunity to survey this complex and vast repertoire. In the present work, we describe a set of tools for the analysis of antibody repertoires and their application to elucidating the dynamics of the response to viral vaccination in human volunteers. By analyzing data from 38 separate blood samples across 2 y, we found that the use of the germ-line library of V and J segments is conserved between individuals over time. Surprisingly, there appeared to be no correlation between the use level of a particular VJ combination and degree of expansion. We found the antibody RNA repertoire in each volunteer to be highly dynamic, with each individual displaying qualitatively different response dynamics. By using combinatorial phage display, we screened selected VH genes paired with their corresponding VL library for affinity against the vaccine antigens. Altogether, this work presents an additional set of tools for profiling the human antibody repertoire and demonstrates characterization of the fast repertoire dynamics through time in multiple individuals responding to an immune challenge.

Barcoding cells using cell-surface programmable DNA-binding domains.

We report an approach to barcode cells through cell-surface expression of programmable zinc-finger DNA-binding domains (surface zinc fingers, sZFs). We show that sZFs enable sequence-specific labeling of living cells by dsDNA, and we develop a sequential labeling approach to image more than three cell types in mixed populations using three fluorophores. We demonstrate the versatility of sZFs through applications in which they serve as surrogate reporters, function as selective cell capture reagents and facilitate targeted cellular delivery of viruses.

A public resource facilitating clinical use of genomes.

Rapid advances in DNA sequencing promise to enable new diagnostics and individualized therapies. Achieving personalized medicine, however, will require extensive research on highly reidentifiable, integrated datasets of genomic and health information. To assist with this, participants in the Personal Genome Project choose to forgo privacy via our institutional review board- approved "open consent" process. The contribution of public data and samples facilitates both scientific discovery and standardization of methods. We present our findings after enrollment of more than 1,800 participants, including whole-genome sequencing of 10 pilot participant genomes (the PGP-10). We introduce the Genome-Environment-Trait Evidence (GET-Evidence) system. This tool automatically processes genomes and prioritizes both published and novel variants for interpretation. In the process of reviewing the presumed healthy PGP-10 genomes, we find numerous literature references implying serious disease. Although it is sometimes impossible to rule out a late-onset effect, stringent evidence requirements can address the high rate of incidental findings. To that end we develop a peer production system for recording and organizing variant evaluations according to standard evidence guidelines, creating a public forum for reaching consensus on interpretation of clinically relevant variants. Genome analysis becomes a two-step process: using a prioritized list to record variant evaluations, then automatically sorting reviewed variants using these annotations. Genome data, health and trait information, participant samples, and variant interpretations are all shared in the public domain-we invite others to review our results using our participant samples and contribute to our interpretations. We offer our public resource and methods to further personalized medical research.

Anticancer, antiobesity, and anti-inflammatory activity of Artemisia species in vitro.

OBJECTIVE: To investigate the anticancer, anti-inflammatory, and antiobesity activity of methanol extracts of eight distinct species: Artemisia Stolonifera (AST), Artemisia Selengensis (ASE), Artemisia Japonica, Artemisia Montana, Artemisia Capillaris (ACA), Artemisia Sylvatica (ASY), Artemisia Keiskeana (AKE), and Artemisia Scoparia (ASC) in vitro. METHODS: Antiproliferative activity was investigated in human breast cancer estrogen receptor-a positive T47D and negative HS578T cell lines exposed to the extracts at various concentrations (5-200 mg/ mL) for 24, 48, and 72 h. For evaluating the anti-inflammatory activity of the extracts, inhibition of nitrite synthesis was investigated in lipopolysaccharide (LPS)-stimulated cultures of macrophages cells exposed to 10, 50, 100, and 200 mg/mL for 24 h. The antiobesity activity of the extracts was determined as triglyceride content and by a lipolysis assay in differentiated 3T3-L1 cells exposed to the extracts for 72 h at the same concentrations described above. RESULTS: All extracts showed similar antiproliferative activity in a dose- and time-dependent manner in HS578T cells. Although extracts at lower concentrations and shorter times stimulated growth of T47D cells, the antiproliferative effects of the extracts on T47D cells at higher concentrations (> 100 mg/ mL) for 72 h were significantly greater than those of HS578T cells. In case of anti-inflammatory activity, some extracts (AST, ASE, ACA, and AKE) significantly reduced nitric oxide production at higher concentrations in the presence of LPS compared with that in control cells. Antiobesity activity was showed with reducing lipid accumulation significantly (> 50%) at concentrations above 100 mg/mL in most extracts (except AST and ACA). Additionally, AKE and ASC increased lipolysis by 11%-24% compared with that in the control. CONCLUSION: Artemisia spp. demonstrates potential as bioactive food supplements.

Digital RNA allelotyping reveals tissue-specific and allele-specific gene expression in human.

We developed a digital RNA allelotyping method for quantitatively interrogating allele-specific gene expression. This method involves ultra-deep sequencing of padlock-captured single-nucleotide polymorphisms (SNPs) from the transcriptome. We characterized four cell lines established from two human subjects in the Personal Genome Project. Approximately 11-22% of the heterozygous mRNA-associated SNPs showed allele-specific expression in each cell line and 4.3-8.5% were tissue-specific, suggesting the presence of tissue-specific cis regulation. When we applied allelotyping to two pairs of sibling human embryonic stem cell lines, the sibling lines were more similar in allele-specific expression than were the genetically unrelated lines. We found that the variation of allelic ratios in gene expression among different cell lines was primarily explained by genetic variations, much more so than by specific tissue types or growth conditions. Comparison of expressed SNPs on the sense and antisense transcripts suggested that allelic ratios are primarily determined by cis-regulatory mechanisms on the sense transcripts.

A robust approach to identifying tissue-specific gene expression regulatory variants using personalized human induced pluripotent stem cells.

Normal variation in gene expression due to regulatory polymorphisms is often masked by biological and experimental noise. In addition, some regulatory polymorphisms may become apparent only in specific tissues. We derived human induced pluripotent stem (iPS) cells from adult skin primary fibroblasts and attempted to detect tissue-specific cis-regulatory variants using in vitro cell differentiation. We used padlock probes and high-throughput sequencing for digital RNA allelotyping and measured allele-specific gene expression in primary fibroblasts, lymphoblastoid cells, iPS cells, and their differentiated derivatives. We show that allele-specific expression is both cell type and genotype-dependent, but the majority of detectable allele-specific expression loci remains consistent despite large changes in the cell type or the experimental condition following iPS reprogramming, except on the X-chromosome. We show that our approach to mapping cis-regulatory variants reduces in vitro experimental noise and reveals additional tissue-specific variants using skin-derived human iPS cells.

Generation of functional human hepatic endoderm from human induced pluripotent stem cells.

UNLABELLED: With the advent of induced pluripotent stem cell (iPSC) technology, it is now feasible to generate iPSCs with a defined genotype or disease state. When coupled with direct differentiation to a defined lineage, such as hepatic endoderm (HE), iPSCs would revolutionize the way we study human liver biology and generate efficient "off the shelf" models of human liver disease. Here, we show the "proof of concept" that iPSC lines representing both male and female sexes and two ethnic origins can be differentiated to HE at efficiencies of between 70%-90%, using a method mimicking physiological relevant condition. The iPSC-derived HE exhibited hepatic morphology and expressed the hepatic markers albumin and E-cadherin, as assessed by immunohistochemistry. They also expressed alpha-fetoprotein, hepatocyte nuclear factor-4a, and a metabolic marker, cytochrome P450 7A1 (Cyp7A1), demonstrating a definitive endodermal lineage differentiation. Furthermore, iPSC-derived hepatocytes produced and secreted the plasma proteins, fibrinogen, fibronectin, transthyretin, and alpha-fetoprotein, an essential feature for functional HE. Additionally iPSC-derived HE supported both CYP1A2 and CYP3A4 metabolism, which is essential for drug and toxicology testing. CONCLUSION: This work is first to demonstrate the efficient generation of hepatic endodermal lineage from human iPSCs that exhibits key attributes of hepatocytes, and the potential application of iPSC-derived HE in studying human liver biology. In particular, iPSCs from individuals representing highly polymorphic variants in metabolic genes and different ethnic groups will provide pharmaceutical development and toxicology studies a unique opportunity to revolutionize predictive drug toxicology assays and allow the creation of in vitro hepatic disease models.

Correlation between Solid Content and Antioxidant Activities in Umbelliferae Salad Plants.

The aim of this study is to evaluate the antioxidant properties of 70% methanolic extracts and the correlation between several antioxidant activities in selected Umbelliferae plants, based on total phenolic content (TPC) and total flavonoid content (TFC). For Umbelliferae plants extracts, the IC50 of DPPH radical (100 µM) quenching activities for extract, TPC, and TFC were 39∼179 µg dry weight (DW)/mL, 14.08∼38.11 µg TPC/mL, and 0.36∼1.51 µg TFC/mL, respectively. The oxygen radical absorbance capacity (ORAC) of extracts ranged from 11.44 to 42.88 mg Trolox equivalent (TE)/g DW extract, whereas ORAC for TPC and TFC was 47.40∼240.19 mg TE/g and 0.72∼11.22 g TE/g, respectively. The TPC had a superior linear correlation (r2=0.817) with 2,2'-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) values. Of the 14 Umbelliferae plant extracts, Sanicula rubiflora, Sanicula chinensis, Torilis japonica, Torilis scabra, and Angelica fallax showed the strongest antioxidant activity.

Protective effects of neohesperidin and poncirin isolated from the fruits of Poncirus trifoliata on potential gastric disease.

The effects of Poncirus trifoliata (P. trifoliata) (Ponciri Fructus, PF) extract and its constituents such as neohesperidin and poncirin on gastritis in rats and human gastric cancer cells were investigated. The PF 70% ethanol extracts (1 g) showed approximately 11.38% of acid-neutralizing capacities and cytotoxicity (IC50=85.39 microg/mL) against human AGS gastric cancer cells. In addition, neohesperidin exhibited antioxidant activity (IC50=22.31 microg/mL) in the 1,1-diphenyl-2-picryldydrazyl (DPPH) radical-scavenging assay. Neohesperidin (50 mg/kg) and poncirin (100 mg/kg) significantly inhibited 55.0% and 60.0% of HCl/ethanol-induced gastric lesions, respectively, and increased the mucus content. In pylorus ligated rats, neohesperidin (50 mg/kg) significantly decreased the volume of gastric secretion and gastric acid output, and increased the pH. From these results, it could be suggested that neohesperidin and poncirin isolated from PF may be useful for the treatment and/or protection of gastritis.

Inhibition of the intestinal glucose transporter GLUT2 by flavonoids.

We tested whether the dominant intestinal sugar transporter GLUT2 was inhibited by intestinal luminal compounds that are inefficiently absorbed and naturally present in foods. Because of their abundance in fruits and vegetables, flavonoids were selected as model compounds. Robust inhibition of glucose and fructose transport by GLUT2 expressed in Xenopus laevis oocytes was produced by the flavonols myricetin, fisetin, the widely consumed flavonoid quercetin, and its glucoside precursor isoquercitrin [corrected]. IC50s for quercetin, myricetin, and isoquercitirin [corrected]were approximately 200- to 1000-fold less than glucose or fructose concentrations, and noncompetitive inhibition was observed. The two other major intestinal sugar transporters, GLUT5 and SGLT1, were unaffected by flavonoids. Sugar transport by GLUT2 overexpressed in pituitary cells and naturally present in Caco-2E intestinal cells was similarly inhibited by quercetin. GLUT2 was detected on the apical side of Caco-2E cells, indicating that GLUT2 was in the correct orientation to be inhibited by luminal compounds. Quercetin itself was not transported by the three major intestinal glucose transporters. Because the flavonoid quercetin, a food component with an excellent pharmacology safety profile, might act as a potent luminal inhibitor of sugar absorption independent of its own transport, flavonols show promise as new pharmacologic agents in the obesity epidemic.

Potential analgesic and anti-inflammatory activities of Panax ginseng head butanolic fraction in animals.

The present study reports the potential anti-rheumatoid activity of Panax ginseng head part. P. ginseng-head part BuOH fraction (PGHB) was safe in acute toxicity (LD(50)>5000mg/kg) and inhibited the partially acetic acid-induced writhes (approximately 32%, P<0.05) in mice. PGHB (500mg/kg) inhibited the acetic acid-induced extravasation of Evan's blue dye in mice by approximately 20.6% (P<0.05), and was similar to the suppressive effect of ibuprofen (27.7%) as a positive control drug. Also, PGHB reduced the carrageenan-induced paw edema at 3h after oral administration, and suppressed the production of serum IL-6 in CIA mice. This suggests that PGHB has potential analgesic and anti-inflammatory activities, and will be the supporting evidence for the potential anti-rheumatoid activity of Korean P. ginseng-head.

Anti-inflammatory mechanisms of apigenin: inhibition of cyclooxygenase-2 expression, adhesion of monocytes to human umbilical vein endothelial cells, and expression of cellular adhesion molecules.

The aim of this study was to clarify the anti-inflammatory mechanism of apigenin. Apigenin inhibited the collagenase activity involved in rheumatoid arthritis (RA) and suppressed lipopolysaccharide (LPS)-induced nitric oxide (NO) production in a dose dependent manner in RAW 264.7 macrophage cells. Pretreatment with apigenin also attenuated LPS-induced cyclooxygenase-2 (COX-2) expression. In addition, apigenin profoundly reduced the tumor necrosis factor-alpha (TNF-alpha)-induced adhesion of monocytes to HUVEC monolayer. Apigenin significantly suppressed the TNF-alpha-stimulated upregulation of vascular cellular adhesion molecule-1 (VCAM-1)-, intracellular adhesion molecule-1 (ICAM-1)-, and E-selectin-mRNA to the basal levels. Taken together, these results suggest that apigenin has significant anti-inflammatory activity that involves blocking NO-mediated COX-2 expression and monocyte adherence. These results further suggest that apigenin may be useful for therapeutic management of inflammatory diseases.

Aglycone Isoflavones and Exopolysaccharides Produced by Lactobacillus acidophilus in Fermented Soybean Paste.

Bioconversion of aglycone-formed isoflavones from glycoside-formed isoflavones by commercial lactic acid bacteria in fermented soybean paste was evaluated. Enterococcus faecium KCTC 13410 showed the most resistant capacity and Lactobacillus acidophilus KCTC 3925 had a sensitive susceptibility at a high NaCl concentration (13.2%) in fermented soybean paste. Among the 5 strains tested, Lac. acidophilus KCTC 3925 showed the highest relative ratio of aglycone-formed isoflavones to total isoflavones in fermented soybean paste. Production of exopolysaccarides (EPS) by lactic acid bacteria was compared using de Man, Rogosa, and Sharpe medium containing 1% sucrose at 37°C for 48 h. Among the 5 lactic acid bacteria, Lac. acidophilus KCTC 3925 and Lactobacillus rhamnosus KCTC 3929 were investigated to produce EPS. Based on the results concerning growing susceptibility and conversion of aglycone-formed isoflavones/EPS production, it is anticipated that Lac. acidophilus KCTC 3925 may be used for preparation of Cheonggukjang, which contains relative low NaCl content.

Antiobesity Effects of Salvia plebeia R. Br. Extract in High-Fat Diet-Induced Obese Mice.

This study was designed to investigate the antiobesity effects of Salvia plebeia R. Br. ethanolic extracts (SPE) in mice fed high-fat diets (HFD). Male C57BL/6J mice were randomly assigned to four groups: normal diet (Chow), high-fat diet (HFD, 45% fat), HFD+SPE 200 (200 mg/kg b.w.), and HFD+SPE 400 (400 mg/kg b.w.). Extracts were administered orally every day for 8 weeks. Increases in body/fat weight and feed efficiency ratio were monitored in all mice. In addition, obesity resulting from feeding HFD to the mice was confirmed by the increase of glucose level, aspartate transaminase, alanine transaminase, triglyceride (TG), high-density lipoprotein cholesterol, very low-density lipoprotein-c, leptin, and adiponectin in blood. The SPE-treated mice gained less body and mesenteric/subcutaneous adipose tissues weights and had lower TG, very low-density lipoprotein cholesterol, leptin, and glucose level in serum, compared to the HFD group. Moreover, histopathological examinations revealed that the size of adipocytes in liver and adipose tissue was significantly decreased by SPE, compared to the HFD group. The expression of adipogenesis transcription factors (e.g., peroxisome proliferator activated receptor γ and CCAAT/enhancer binding protein α) and lipogenesis-related target genes (adipocyte fatty acid-binding protein 2, lipoprotein lipase, fatty acid synthase, and sterol regulatory element-binding transcription factor 1c) in HFD-induced obese mice was decreased by SPE treatment. These results suggest that SPE attenuates the fat accumulation in HFD-induced obese mice by suppressing the expressions of genes related to adipogenesis and lipogenesis activity. Therefore, SPE could be developed as a potential therapy for reduction of body weight and antiobesity intervention.

Ultrasound-guided evacuation of spontaneous intracerebral hematoma in the basal ganglia.

Real-time ultrasonography was used for the aspiration of intracerebral hematoma in the basal ganglia in 7 patients without signs of impending herniation. The ultrasound device was used to select the trajectory and target point for hematoma drainage. Ultrasound-guided aspiration was performed via a burr hole under local anesthesia. We used a temporal burr hole entry point rather than the more frequently used pre-coronal approach. The burr hole was positioned 4 to 6 cm behind the posterior border of the frontal process of the zygomatic bone and 4 to 5 cm above the external auditory meatus. The hematoma was then evacuated with urokinase irrigation. Ultrasound guidance allows simple, precise localization of the hematoma and the distance from the surface to the target can be calculated. Ultrasound-guided catheter placement for fibrinolysis and hematoma drainage is a simple and safe procedure.