Query: Important of business records

title: Matching State Business Registration Records to Census Business Data
abstract: We describe our methodology and results from matching state Business Registration Records (BRR) to Census business data. We use data from Massachusetts and California to develop methods and preliminary results that could be used to guide matching data for additional states. We obtain matches to Census business records for 45% of the Massachusetts BRR records and 40% of the California BRR records. We find higher match rates for incorporated businesses and businesses with higher startup quality scores as assigned in Guzman and Stern (2018) Clerical reviews show that using relatively strict matching on address is important for match accuracy, while results are less sensitive to name matching strictness. Among matched BRR records, the modal timing of the first match to the BR is in the year in which the BRR record was filed. We use two sets of software to identify matches: SAS DQ Match and a machine learning algorithm described in Cuffe and Goldschlag (2018) We find preliminary evidence that while the ML based method yields more match results, SAS DQ tends to result in higher accuracy rates. To conclude, we provide suggestions on how to proceed with matching other states' data in light of our findings using these two states.

title: Prediction model of business success or failure for Palestinian small enterprises in the West Bank
abstract: The aim of this study is to examine the factors that lead to success or failure of a small business in the West Bank of Palestine.,This study methodology is a survey research, testing the Lussier model of business success and failure with a sample of 246 small businesses (90 failed and 156 successful) to better understand the reasons of their success or failure using logistic regression statistical analysis.,The model is significant (p 0.000) it will predict a group of businesses as successful or failed more accurately than random guessing 99 per cent of the time. The model will also predict a specific small firm as successful or failed 94 per cent of the time vs. 50 per cent for random guessing. The r square is very high (r 0.70) indicating that the model variables are, in fact, significant predictors of success or failure. Results indicate that having adequate capital, keeping good records with financial controls, making plans and getting professional advice on how to manage the firm are the most important factors for the viability and success of small businesses.,With the high rate of small business failure globally, results of this study provide a list of variables that contribute to the success of small firms. Firms that focus on these important factors will increase their odds of success. Thus, avoiding failure, firms better utilize resources that contribute to economic growth.,This is the first study that looks at success and failure of small businesses in Palestine. There is no one single accepted theory that may be applied to small businesses. This paper aims to further contribute to the global validity of Lussier success and failure model moving toward a theory to better understand why some businesses succeed and others fail.

title: Automatic Identification of Important Segments and Expressions for Mining of Business Oriented Conversations at Contact Centers
abstract: Textual records of business oriented conversations between customers and agents need to be analyzed properly to acquire useful business insights that improve productivity. For such an analysis, it is critical to identify appropriate textual segments and expressions to focus on, especially when the textual data consists of complete transcripts, which are often lengthy and redundant. In this paper, we propose a method to identify important segments from the conversations by looking for changes in the accuracy of a categorizer designed to separate different business outcomes. We extract effective expressions from the important segments to define various viewpoints. In text mining a viewpoint defines the important associations between key entities and it is crucial that the correct viewpoints are identified. We show the effectiveness of the method by using real datasets from a car rental service center.

title: The HealthChain Blockchain for Electronic Health Records: Development Study
abstract: Health care professionals are required to maintain accurate health records of patients. Furthermore, these records should be shared across different health care organizations for professionals to have a complete review of medical history and avoid missing important information. Nowadays, health care providers use electronic health records (EHRs) as a key to the implementation of these goals and delivery of quality care. However, there are technical and legal hurdles that prevent the adoption of these systems, such as concerns about performance and privacy issues.  This study aimed to build and evaluate an experimental blockchain for EHRs, named HealthChain, which overcomes the disadvantages of traditional EHR systems.  HealthChain is built based on consortium blockchain technology. Specifically, three organizations, namely hospitals, insurance providers, and governmental agencies, form a consortium that operates under a governance model, which enforces the business logic agreed by all participants. Every peer node hosts an instance of the distributed ledger consisting of EHRs and an instance of chaincode regulating the permissions of participants. Designated orderers establish consensus on the order of EHRs and then disseminate blocks to peers.  HealthChain achieves functional and nonfunctional requirements. It can store EHRs in a distributed ledger and share them among different participants. Moreover, it demonstrates superior features, such as privacy preservation, security, and high throughput. These are the main reasons why HealthChain is proposed.  Consortium blockchain technology can help to build new EHR systems and solve the problems that prevent the adoption of traditional systems.

title: The archaeology of business networks: the use of archival records in case study research
abstract: The case study in the Industrial Marketing and Purchasing (IMP) tradition relies almost exclusively on in depth interviews as the main source of data. This article discusses the use of another strategy, namely archival research. The first section of the article argues that archival research is an important but often overlooked tool in case study research. Using archival material provides longitudinal data, generates new theories about business networks and improves the validity and reliability of findings. In the second section of the article a model of archival research is presented and strategies for managing the process are discussed.

title: The business case for implementing electronic health records in primary care settings in the United States
abstract: With escalating health care costs in the United States, it is easy to understand why health care service providers operations such as hospitals, primary care physician practices are heavily focused on controlling cost of services to consumers. But there is another very important component to the health care service providers profit equation revenue. There are many things a health care service provider can do to influence revenue, and one of the most important is implementation of Electronic Health Records. Electronic health record (EHR) systems hold substantial promise for improving the quality of health care in the United States while decreasing costs. Despite such promise, adoption rates for these systems in the United States remain quite low, particularly among primary care physicians, with funding often cited as the most significant barrier to their adoption. This study analyses the costs and benefits of EHR systems and presents a cost benefit model for making the business case for their implementation in primary care settings in the United States for increased revenues and quality services.

title: Probabilistic modeling personalized treatment pathways using electronic health records
abstract: Modeling personalized treatment pathways plays an important role in understanding essential/critical treatment behaviors performed on patients during their hospitalizations and thus provides the opportunity for the improvement of better health service delivery in treatment pathways.  Unlike traditional business process mining, modeling personalized treatment pathways is more challenging because they are typically case specific. Although several studies have been devoted to modeling patient treatment pathways, limited efforts have been made on the extraction of latent semantics and their transitions behind patient treatment pathways, which are often ambiguous and poorly understood.  In this article, we propose an extension of the Hidden Markov Model to mine and model personalized treatment pathways by extracting latent treatment topics and identifying their sequential dependencies in pathways, in the form of probabilistic distributions and transitions of patients' raw Electronic Health Record (EHR) data.  We evaluated the proposed model on 48,024 patients with cardiovascular diseases. A total of 15 treatment topics and their typical transition routes were discovered from EHR data that contained 1,391,251 treatment events with 2786 types of interventions and that were evaluated by ten clinicians manually. The obtained p values are 0.000146 and 0.009106 in comparison with both Latent Dirichlet Allocation and Sequent Naive Bayes models, respectively; this outcome indicate that our approach achieves a better understanding of human evaluators on modeling personalized treatment pathway than that of benchmark models.  The experimental results on a real world data set clearly suggest that the proposed model has efficiency in mining and modeling personalized treatment pathways. We argue that the discovered treatment topics and their transition routes, as actionable knowledge that represents the practice of treating individual patients in their clinical pathways, can be further exploited to help physicians better understand their specialty and learn from previous experiences for treatment analysis and improvement.

title: Modelling the Adaptation of Business Continuity Planning by Businesses Using Neural Networks
abstract: Business continuity planning is an important element of business continuity management and is regarded as a fundamental step towards reducing the negative impacts of business disruptions caused by internal and external hazardous events. Many businesses are not prepared for such events, and very few studies have tried to examine and model the factors that contribute to business continuity management planning by various companies. In this paper we propose and develop a feed forward neural network for modelling businesses continuity planning by businesses based on a dataset of 283 businesses operating in the Greater Toronto Area in Ontario, Canada. The fully connected neural network applied was trained on 65 of the dataset records using different subsets of input variables. In order to preserve the generalization ability of the trained network, 15 of the dataset records were used as a validation set for early stopping during the network's training process. Prediction capability of the trained networks was evaluated on 20 and never seen records of the dataset. The classification ability of the networks was then analysed using receiver operating characteristic and detection error trade off curves, where the results obtained were promising. The equal error rate for the best models was 12 which reflects a very good accuracy of these models in predicting the existence of business continuity planning for a generic company.

title: Using business intelligence and data visualization to understand the characteristics of failed appointments in dental school clinics.
abstract: Broken appointments are an important cause of waste in health care. Patients who fail to attend incur costs to providers, deny trainees learning opportunities, and impact their own health as well as that of other patients who are waiting for care.  A total of 410,000 appointment records over 3 years were extracted from our electronic health record. We conducted exploratory data analysis and assessed correlations between appointment no shows and other attributes of the appointment and the patient. The University of Michigan Medical School's Committee on Human Research reviewed the study and deemed that no Institutional Review Board oversight was necessary for this quality improvement project that was, retrospectively, turned into a study with previously de identified data.  The patient's previous attendance record is the single most significant correlation with attendance. We found that patients who said they are "scared" of dental visits were 62% as likely to attend as someone reporting "no problem." Patients over 65 years of age have better attendance rates. There was a positive association between receiving email/text confirmation and attendance. A total of 94.9% of those emailed a reminder and 92.2% of those who were texted attended their appointment. CONCLUSION(S) We were able to identify relationships of several variables to failed and attended appointments that we were previously unknown to us. This knowledge enabled us to implement interventions to support better attendance at Dental Clinics at the University of Michigan, improving patient health, student training, and efficient use of resources.

title: A Proposed Framework to Investigate the User Acceptance of Personal Health Records in Malaysia using UTAUT2 and PMT
abstract: Personal Health Records (PHRs) can be considered as one of the most important health technologies. PHRs enroll the patients directly to their health decision making through giving them the authority to control and share their health information. Testing the user acceptance toward new technology is a vital process. Over the previous decades many models have been used and the latest one was UTAUT2. UTAUT2 have been widely used in e business and gaming user acceptance researches, whereas, it has been rarely used in health field. This study proposes a combination of UTAUT2 and PMT in order to investigate the user acceptance of PHRs. Relevant factors may be added at the literature review stage. The final model will be used as a framework to investigate the user acceptance of PHRs in Malaysia.