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ObjectivesQualitative research aimed at identifying patient acceptance of active surveillance (AS) has been identified as a public health research priority. The primary objective of this study was to determine if analysis of a large-sample of anonymous internet conversations (ICs) could be utilized to identify unmet public needs regarding AS.MethodsEnglish-language ICs regarding prostate cancer (PC) treatment with AS from 2002–12 were identified using a novel internet search methodology. Web spiders were developed to mine, aggregate, and analyze content from the world-wide-web for ICs centered on AS. Collection of ICs was not restricted to any specific geographic region of origin. NLP was used to evaluate content and perform a sentiment analysis. Conversations were scored as positive, negative, or neutral. A sentiment index (SI) was subsequently calculated according to the following formula to compare temporal trends in public sentiment towards AS: [(# Positive IC/#Total IC) - (#Negative IC/#Total IC) x 100].ResultsA total of 464 ICs were identified. Sentiment increased from -13 to +2 over the study period. The increase sentiment has been driven by increased patient emphasis on quality-of-life factors and endorsement of AS by national medical organizations. Unmet needs identified in these ICs include: a gap between quantitative data regarding long-term outcomes with AS vs. conventional treatments, desire for treatment information from an unbiased specialist, and absence of public role models managed with AS.ConclusionsThis study demonstrates the potential utility of online patient communications to provide insight into patient preferences and decision-making. Based on our findings, we recommend that multidisciplinary clinics consider including an unbiased specialist to present treatment options and that future decision tools for AS include quantitative data regarding outcomes after AS.

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Customer experience (CX) is an important contributor to business performance. Yet, studies vary in defining CX, variables and constructs, and measurement to performance. This study defined CX and the direct impact to market capitalization (MC) of US public companies. The CX definition combined Customer Experience Quality (CEQ) with Customer Satisfaction (CSAT), Customer Loyalty (CLY), and Net Promoter Score (NPS). Each dimension was measured to the extent to which they impact MC. The sample consisted of 1605 US customers of US publicly traded companies. Using correlation analysis, multiple linear regression, and confirmatory factor analysis, the individual variables of CEQ were positively and significantly correlated with MC and with each other. CEQ, CSAT, CLY, and NPS were also positively and significantly correlated with MC, and the holistic CX was predictive of MC. Companies can apply the insights of the study to improve their customer experience to deliver measurable business and shareholder value.

Data scientists and statisticians are often at odds when determining the best approaches and choosing between machine learning and statistical modeling to solve their analytical challenges and problem statements across industries. However, machine learning and statistical modeling are actually more closely related to each other rather than being on different sides of an analysis battleground. The decision on which approach to choose is often based on the problem at hand, expected outcome(s), real world application of the results and insights, as well as the availability and granularity of data for the analysis. Overall machine learning and statistical modeling are complementary techniques that are guided on similar mathematical principles, but leverage different tools to arrive at insights. Determining the best approach should consider the problem to be solved, empirical evidence and resulting hypothesis, data sources and their completeness, number of variables/data elements, assumptions, and expected outcomes such as the need for predictions or causality and reasoning. Experienced analysts and data scientists are often well versed in both types of approaches and their applications, hence use best suited tools for their analytical challenges. Due to the importance and relevance of the subject in the current analytics environment, this chapter will present an overview of each approach as well as outline their similarities and differences to provide the needed understanding when selecting the proper technique for problems at hand. Furthermore, the chapter will also provide examples of applications in the healthcare industry and outline how to decide which approach is best when analyzing healthcare data. Understanding of the best suited methodologies can help the healthcare industry to develop and apply advanced analytical tools to speed up the diagnostic and treatment processes as well as improve the quality of life for their patients.