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Recent research in the area of value creation in online health communities is gaining importance due to various academic and commercial perspectives. Positive patient reviews attracts and retains patients. Physicians and health care providers are aware that a shift in the patient selection mechanism is taking place. One problem is the validation of the patient review. Specifically, how much impact should such review have on other patients as they browse different online health communities? This paper proposes a new algorithm that ranks each reviewer by weighting the type of feedback they provide for each type of service providers. The weighting is for both “contribution toâ€, and “engagement withâ€, the online community. The “contribution to†variables are A) number of comments, B) number of check-ins, and C) Stars rating left for the provider. Those variables help creating a “Contribution Weight†for the reviewer. Another weight, the “Engagement weight†is calculated based on A) the number of reactions and B) comments to other reviews. All variables are represented as a binary matrixes to be able to calculate the corresponding Eigen vector and generate a weight for the reviewers based on those variables. The advantage of calculating the weight in this fashion is that it allows the reviewer total weight to impact the evaluation of the service provider even if that reviewer submitted review did not receive any positive feedback yet from the community, but it is based on the reviewer prior interaction. The algorithm variables are selected based on two datasets. First, the data available from Medicare.gov which provide information about groups, individual physicians, and other clinicians currently enrolled in Medicare. Second, is from Yelp dataset, which provides a subset of business, along with the stored attributes.

Online communities, Health , creating social value