Growing dental practices

2021 ◽  
Vol 17 (12) ◽  
pp. 608-609
Author(s):  
Mike Hennan

Mike Hennan explains how you can increase your patient lifetime value

1973 ◽  
Vol 37 (1) ◽  
pp. 26-29
Author(s):  
EM Speed ◽  
A Wolff ◽  
JH Barr ◽  
JL Bomba ◽  
RW Rule

2020 ◽  
Vol 33 (2) ◽  
pp. 102-105
Author(s):  
Joanna Bialowska ◽  
Witold Bojar ◽  
Tomasz Zareba ◽  
Stefan Tyski ◽  
Barbara Tymczyna-Borowicz

AbstractCross-infection involves the transmission of microorganisms through secretions, bodily fluids and excreta, as well as undisinfected surfaces and medical equipment. In the dental office, diseases are transmitted via various routes, e.g. from patient to dentist or other member of dental team, from doctor or dental team member to patient, from patient to another patient, from dental office to community and from community to patient. The study was conducted to evaluate the effectiveness of infection control in dental practices based on the qualitative and quantitative assessment of microbiological contaminants detected on the surface of filling material packaging used in dental offices. The material for research were 9 packages containing dental materials during their use in 3 dental settings. The packages were placed in sterile flasks and rinsed to wash microorganisms from the surfaces. The washes were filtered through membrane filters and cultured under proper aerobic and anaerobic conditions, and at elevated CO2 concentration. Microbial growth on TIO and TSB media was observed. The contamination of most samples remained low as indicated by the growth from one to a maximum of five colonies on TSA. The contamination remained at the level of 10-50 CFU/package, i.e. <100 CFU/single package. The tests evaluating the contamination of dental package surfaces with aerobic bacteria confirmed high hygiene standards observed in dental offices from which the packages were brought.


2020 ◽  
Vol 11 (SPL1) ◽  
pp. 628-631
Author(s):  
Devangi Agrawal ◽  
Namisha Khara ◽  
Bhushan Mundada ◽  
Nitin Bhola ◽  
Rajiv Borle

In the wake of the current outbreak of novel Covid-19, which is now declared as a 'pandemic' by the WHO, people around the globe have been dealing with a lot of difficulties. This virus had come into light in December 2019 and since then has only grown exponentially. Amongst the most affected are the ones who have been working extremely hard to eradicate it, which includes the hospitals, dental fraternity and the health-care workers. These people are financially burdened due to limited practise. In the case of dentistry, to avoid the spread of the virus, only emergency treatments are being approved, and the rest of the standard procedures have been put on hold. In some cases, as the number of covid cases is rising, many countries are even trying to eliminate the emergency dental procedures to divert the finances towards the treatment of covid suffering patients. What we need to realise is that this is probably not the last time that we are facing such a situation. Instead of going down, we should set up guidelines with appropriate precautionary measures together with the use of standardised PPEs. The government should also establish specific policies to support dental practices and other health-care providers. Together, we can fight this pandemic and come out stronger.


2021 ◽  
pp. 1-10
Author(s):  
Ahmet Tezcan Tekin ◽  
Tolga Kaya ◽  
Ferhan Cebi

The use of fuzzy logic in machine learning is becoming widespread. In machine learning problems, the data, which have different characteristics, are trained and predicted together. Training the model consisting of data with different characteristics can increase the rate of error in prediction. In this study, we suggest a new approach to assembling prediction with fuzzy clustering. Our approach aims to cluster the data according to their fuzzy membership value and model it with similar characteristics. This approach allows for efficient clustering of objects with more than one cluster characteristic. On the other hand, our approach will enable us to combine boosting type ensemble algorithms, which are various forms of assemblies that are widely used in machine learning due to their excellent success in the literature. We used a mobile game’s customers’ marketing and gameplay data for predicting their customer lifetime value for testing our approach. Customer lifetime value prediction for users is crucial for determining the marketing cost cap for companies. The findings reveal that using a fuzzy method to ensemble the algorithms outperforms implementing the algorithms individually.


2012 ◽  
Vol 40 (7) ◽  
pp. 1057-1064 ◽  
Author(s):  
Wen Chang ◽  
Chen Chang ◽  
Qianpin Li

The concept of regarding customers as assets that should be managed and whose value should be measured is now accepted and recognized by academics and practitioners. This focus on customer relationship management makes it extremely important to understand customer lifetime value (CLV) because CLV models are an efficient and effective way to evaluate a firm's relationship with its customers. Assessment of CLV is especially important for firms in implementing customer-oriented services. In this paper we provide a critical review of the literature on the development process and applications of CLV.


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