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(1) Background: The appearance of physician rating websites (PRWs) has raised researchers’ interest in the online healthcare field, particularly how users consume information available on PRWs in terms of online physician reviews and providers’ information in their decision-making process. The aim of this study is to consistently review the early scientific literature related to digital healthcare platforms, summarize key findings and study features, identify literature deficiencies, and suggest digital solutions for future research. (2) Methods: A systematic literature review using key databases was conducted to search published articles between 2010 and 2020 and identified 52 papers that focused on PRWs, different signals in the form of PRWs’ features, the findings of these studies, and peer-reviewed articles. The research features and main findings are reported in tables and figures. (3) Results: The review of 52 papers identified 22 articles for online reputation, 15 for service popularity, 16 for linguistic features, 15 for doctor–patient concordance, 7 for offline reputation, and 11 for trustworthiness signals. Out of 52 studies, 75% used quantitative techniques, 12% employed qualitative techniques, and 13% were mixed-methods investigations. The majority of studies retrieved larger datasets using machine learning techniques (44/52). These studies were mostly conducted in China (38), the United States (9), and Europe (3). The majority of signals were positively related to the clinical outcomes. Few studies used conventional surveys of patient treatment experience (5, 9.61%), and few used panel data (9, 17%). These studies found a high degree of correlation between these signals with clinical outcomes. (4) Conclusions: PRWs contain valuable signals that provide insights into the service quality and patient treatment choice, yet it has not been extensively used for evaluating the quality of care. This study offers implications for researchers to consider digital solutions such as advanced machine learning and data mining techniques to test hypotheses regarding a variety of signals on PRWs for clinical decision-making.

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This paper presents a low power Gaussian Frequency-Shift Keying (GFSK) transceiver (TRX) with high efficiency power management unit and integrated Single-Pole Double-Throw switch for Bluetooth low energy application. Receiver (RX) is implemented with the RF front-end with an inductor-less low-noise transconductance amplifier and 25% duty-cycle current-driven passive mixers, and low-IF baseband analog with a complex Band Pass Filter(BPF). A transmitter (TX) employs an analog phase-locked loop (PLL) with one-point GFSK modulation and class-D digital Power Amplifier (PA) to reduce current consumption. In the analog PLL, low power Voltage Controlled Oscillator (VCO) is designed and the automatic bandwidth calibration is proposed to optimize bandwidth, settling time, and phase noise by adjusting the charge pump current, VCO gain, and resistor and capacitor values of the loop filter. The Analog Digital Converter (ADC) adopts straightforward architecture to reduce current consumption. The DC-DC buck converter operates by automatically selecting an optimum mode among triple modes, Pulse Width Modulation (PWM), Pulse Frequency Modulation (PFM), and retention, depending on load current. The TRX is implemented using 1P6M 55-nm Complementary Metal–Oxide–Semiconductor (CMOS) technology and the die area is 1.79 mm2. TRX consumes 5 mW on RX and 6 mW on the TX when PA is 0-dBm. Measured sensitivity of RX is −95 dBm at 2.44 GHz. Efficiency of the DC-DC buck converter is over 89% when the load current is higher than 2.5 mA in the PWM mode. Quiescent current consumption is 400 nA from a supply voltage of 3 V in the retention mode.