A 5.8 GHz 1.8 V +20 dBm 32.5% PAE Power Amplifier for a Short-Range Over-the-Air WPT Application.

This paper presents a 5.8 GHz differential cascode power amplifier for an over-the-air wireless power transfer application. Over-the-air wireless power transfer provides a variety of benefits in several applications such as the Internet of Things and medical implantation applications. The proposed PA features two fully differentially active stages with a custom-designed transformer to provide a single-ended output. The custom-made transformer shows a high quality factor, as high as 11.6 and 11.2 for the primary and secondary sides at 5.8 GHz. Fabricated using a standard 180 nm CMOS process, the amplifier achieves input and output matching of -14.7 dB and -29.7 dB, respectively. To achieve a high power level and efficiency, accurate optimization through power matching, Power Added Efficiency (PAE), and the design of the transformer are carried out while the supply voltage is limited to 1.8 V. Measurement results show a 20 dBm output power with a PAE as high as 32.5%, which makes the PA suitable for application, and it can be implanted while arrayed with various antenna arrays. Finally, a FOM is introduced to compare the performance of the work with similar works in the literature.

A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain.

Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, places, and organizations, as predefined semantic types in electronic health records (EHR). bNER is essential for discovering novel knowledge using computational methods and Information Technology. Early bNER systems were configured manually to include domain-specific features and rules. However, these systems were limited in handling the complexity of the biomedical text. Recent advances in deep learning (DL) have led to the development of more powerful bNER systems. DL-based bNER systems can learn the patterns of biomedical text automatically, making them more robust and efficient than traditional rule-based systems. This paper reviews the healthcare domain of bNER, using DL techniques and artificial intelligence in clinical records, for mining treatment prediction. bNER-based tools are categorized systematically and represent the distribution of input, context, and tag (encoder/decoder). Furthermore, to create a labeled dataset for our machine learning sentiment analyzer to analyze the sentiment of a set of tweets, we used a manual coding approach and the multi-task learning method to bias the training signals with domain knowledge inductively. To conclude, we discuss the challenges facing bNER systems and future directions in the healthcare field.

Design of a Current Sensing System with TIA Gain of 160 dBΩ and Input-Referred Noise of 1.8 pArms for Biosensor.

This paper proposes a high-gain low-noise current signal detection system for biosensors. When the biomaterial is attached to the biosensor, the current flowing through the bias voltage is changed so that the biomaterial can be sensed. A resistive feedback transimpedance amplifier (TIA) is used for the biosensor requiring a bias voltage. Current changes in the biosensor can be checked by plotting the current value of the biosensor in real time on the self-made graphical user interface (GUI). Even if the bias voltage changes, the input voltage of the analog to digital converter (ADC) does not change, so it is designed to plot the current of the biosensor accurately and stably. In particular, for multi-biosensors with an array structure, a method of automatically calibrating the current between biosensors by controlling the gate bias voltage of the biosensors is proposed. Input-referred noise is reduced using a high-gain TIA and chopper technique. The proposed circuit achieves 1.8 pArms input-referred noise with a gain of 160 dBΩ and is implemented in a TSMC 130 nm CMOS process. The chip area is 2.3 mm2, and the power consumption of the current sensing system is 12 mW.

A Design of Analog Front-End with DBPSK Demodulator for Magnetic Field Wireless Network Sensors.

This paper presents an on-chip fully integrated analog front-end (AFE) with a non-coherent digital binary phase-shift keying (DBPSK) demodulator suitable for short-range magnetic field wireless communication applications. The proposed non-coherent DBPSK demodulator is designed based on using comparators to digitize the received differential analog BPSK signal. The DBPSK demodulator does not need any phase-lock loop (PLL) to detect the data and recover the clock. Moreover, the proposed demodulator provides the detected data and the recovered clock simultaneously. Even though previous studies have offered the basic structure of the AFEs, this work tries to amplify and generate the required differential BPSK signal without missing data and clock throughout the AFE, while a low voltage level signal is received at the input of the AFE. A DC-offset cancellation (DCOC), a cascaded variable gain amplifier (VGA), and a single-to-differential (STOD) converter are employed to construct the implemented AFE. The simulation results indicate that the AFE provides a dynamic range of 0 dB to 40 dB power gain with 2 dB resolution. Measurement results show the minimum detectable voltage at the input of AFE is obtained at 20 mV peak-to-peak. The AFE and the proposed DBSPK demodulator are analyzed and fabricated in a 130 nm Bipolar-CMOS-DMOS (BCD) technology to recover the maximum data rate of 32 kbps where the carrier frequency is 128 kHz. The implemented DCOC, cascaded VGA, STOD, and the demodulator occupy 0.15 mm2, 0.063 mm2, 0.045 mm2, and 0.03 mm2 of area, respectively. The AFE and the demodulator consume 2.9 mA and 0.15 mA of current from an external 5 V power supply, respectively.

A Design of 10-Bit Asynchronous SAR ADC with an On-Chip Bandgap Reference Voltage Generator.

A proposed prototype of a 10-bit 1 MS/s single-ended asynchronous Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) with an on-chip bandgap reference voltage generator is fabricated with 130 nm technology. To optimize the power consumption, static, and dynamic performance, several techniques have been proposed. A dual-path bootstrap switch was proposed to increase the linearity sampling. The Voltage Common Mode (VCM)-based Capacitive Digital-to-Analog Converter (CDAC) switching technique was implemented for the CDAC part to alleviate the switching energy problem of the capacitive DAC. The proposed architecture of the two-stage dynamic latch comparator provides high speed and low power consumption. Moreover, to achieve faster bit conversion with an efficient time sequence, asynchronous SAR logic with an internally generated clock is implemented, which avoids the requirement of a high-frequency external clock, as all conversions are carried out in a single clock cycle. The proposed error amplifier-based bandgap reference voltage generator provides a stable reference voltage to the ADC for practical implementation. The measurement results of the proposed SAR ADC, including an on-chip bandgap reference voltage generator, show an Effective Number of Bits (ENOB) of 9.49 bits and Signal-to-Noise and Distortion Ratio (SNDR) of 58.88 dB with 1.2 V of power supply while operating with a sampling rate of 1 MS/s.

A High-Power 3P3T Cross Antenna Switch with Low Harmonic Distortion and Enhanced Isolation Using T-Type Pull-Down Path for Cellular Mobile Devices.

This paper presents a radio frequency (RF) triple pole triple throw 3P3T cross antenna switch for cellular mobile devices. The negative biasing scheme was applied to improve the power-handling capability and linearity of the switch by increasing the maximum tolerable voltage drop across the drain and source and reverse biasing the parasitic junction diodes. To avoid signal reflection through the antenna in off-state, all the antenna ports were equipped with 50-ohm termination to provide the pull-down path. Considering the simultaneous operation of antenna ports in different switch cases, the presented T-type pull-down path demonstrated improvement of isolation by over 15 dB. Using stacked switches, the 3P3T handled the input power level of over 35 dBm. The chip was manufactured in 65 nm complementary metal oxide semiconductor (CMOS) silicon on insulator (SOI) technology with a die size of 790 × 730 µm. The proposed structure achieved insertion loss, isolation, and voltage standing wave ratio (VSWR) of less than -0.9 dB, -40 dB, and 1.6, respectively, when the input signal was 3.8 GHz. The measured results prove the implemented switch shows the second and third harmonic distortion performances of less than -60 dBm when the input power level and frequency are 25 dBm and 3.8 GHz, respectively.

A 1.8-2.7 GHz Triple-Band Low Noise Amplifier with 31.5 dB Dynamic Range of Power Gain and Adaptive Power Consumption for LTE Application.

This paper presents a multi-gain radio frequency (RF) front-end low noise amplifier (LNA) utilizing a multi-core based on the source degeneration topology. The LNA can cover a wide range of input and output frequency matching by using a receiver (RX) switch at the input and a capacitor bank at the output of the LNA. In the proposed architecture here, to avoid the saturation of RX chain, 12 gain steps including positive, 0 dB, and negative power gains are controlled by a mobile industry processor interface (MIPI). The multi-core architecture offers the ability to control the power consumption over different gain steps. In order to avoid the phase discontinuity, the negative gain steps are provided using an active amplification and T-type attenuation path that keeps the phase discontinuity below ±5 degrees between two adjacent power gain steps. Using the multi-core structure, the power consumption is optimized in different power gains. The structure is enhanced with the adaptive variable cores and reactance parameters to maintain different power consumption for different gain steps and remain the output matching in an acceptable operating range. Furthermore, auxiliary linearization circuitries are added to improve the input third intercept point (IIP3) performance of the LNA. The chip is fabricated in 65 nm complementary metal-oxide semiconductor (CMOS) silicon on insulator (SOI) process and the die area is 0.308 mm2. The proposed architecture achieves the IIP3 performance of -10.2 dBm and 8.6 dBm in the highest and lowest power gains, which are 20.5 dB and -11 dB, respectively. It offers the noise figure (NF) performance of 1.15 dB in the highest power gain while it reaches 14 dB when the power gain is -11 dB. The LNA consumes 16.8 mA and 1.33 mA current from a 1 V power supply that is provided by an on-chip low-dropout (LDO) when it operates at the highest and lowest gains, respectively.

A Low-Power Analog Processor-in-Memory-Based Convolutional Neural Network for Biosensor Applications.

This paper presents an on-chip implementation of an analog processor-in-memory (PIM)-based convolutional neural network (CNN) in a biosensor. The operator was designed with low power to implement CNN as an on-chip device on the biosensor, which consists of plates of 32 × 32 material. In this paper, 10T SRAM-based analog PIM, which performs multiple and average (MAV) operations with multiplication and accumulation (MAC), is used as a filter to implement CNN at low power. PIM proceeds with MAV operations, with feature extraction as a filter, using an analog method. To prepare the input feature, an input matrix is formed by scanning a 32 × 32 biosensor based on a digital controller operating at 32 MHz frequency. Memory reuse techniques were applied to the analog SRAM filter, which is the core of low power implementation, and in order to accurately grasp the MAC operational efficiency and classification, we modeled and trained numerous input features based on biosignal data, confirming the classification. When the learned weight data was input, 19 mW of power was consumed during analog-based MAC operation. The implementation showed an energy efficiency of 5.38 TOPS/W and was differentiated through the implementation of 8 bits of high resolution in the 180 nm CMOS process.

A Dual-Band 47-dB Dynamic Range 0.5-dB/Step DPA with Dual-Path Power-Combining Structure for NB-IoT.

This paper presents a digital power amplifier (DPA) with a 43-dB dynamic range and 0.5-dB/step gain steps for a narrow-band Internet of Things (NBIoT) transceiver application. The proposed DPA is implemented in a dual-band architecture for both the low band and high band of the frequency coverage in an NBIoT application. The proposed DPA is implemented in two individual paths, power amplification, and power attenuation, to provide a wide range when both paths are implemented. To perform the fine control over the gain steps, ten fully differential cascode power amplifier cores, in parallel with a binary sizing, are used to amplify power and enable signals and provide fine gain steps. For the attenuation path, ten steps of attenuated signal level are provided which are controlled with ten power cores, similar to the power amplification path in parallel but with a fixed, small size for the cores. The proposed implementation is finalized with output custom-made baluns at the output. The technique of using parallel controlled cores provides a fine power adjustability by using a small area on the die where the NBIoT is fabricated in a 65-nm CMOS technology. Experimental results show a dynamic range of 47 dB with 0.5-dB fine steps are also available.

A Configurable and Fully Synthesizable RTL-Based Convolutional Neural Network for Biosensor Applications.

This paper presents a register-transistor level (RTL) based convolutional neural network (CNN) for biosensor applications. Biosensor-based diseases detection by DNA identification using biosensors is currently needed. We proposed a synthesizable RTL-based CNN architecture for this purpose. The adopted technique of parallel computation of multiplication and accumulation (MAC) approach optimizes the hardware overhead by significantly reducing the arithmetic calculation and achieves instant results. While multiplier bank sharing throughout the convolutional operation with fully connected operation significantly reduces the implementation area. The CNN model is trained in MATLAB® on MNIST® handwritten dataset. For validation, the image pixel array from MNIST® handwritten dataset is applied on proposed RTL-based CNN architecture for biosensor applications in ModelSim®. The consistency is checked with multiple test samples and 92% accuracy is achieved. The proposed idea is implemented in 28 nm CMOS technology. It occupies 9.986 mm2 of the total area. The power requirement is 2.93 W from 1.8 V supply. The total time taken is 8.6538 ms.

A Highly Efficient RF-DC Converter for Energy Harvesting Applications Using a Threshold Voltage Cancellation Scheme.

In this paper, a self-threshold voltage (Vth) compensated Radio Frequency to Direct Current (RF-DC) converter operating at 900 MHz and 2.4 GHz is proposed for RF energy harvesting applications. The threshold voltage of the rectifying devices is compensated by the bias voltage generated by the auxiliary transistors and output DC voltage. The auxiliary transistors compensate the threshold voltage (Vth) of the PMOS rectifying device while the threshold voltage (Vth) of the NMOS rectifying device is compensated by the output DC voltage. The proposed RF-DC converter was implemented in 180 nm Complementary Metal-Oxide Semiconductor (CMOS) technology. The experimental results show that the proposed design achieves better performance at both 900 MHz and 2.4 GHz frequencies in terms of PCE, output voltage, sensitivity, and effective area. The peak power conversion efficiency (PCE) of 38.5% at -12 dBm across a 1 MΩ load for 900 MHz frequency was achieved. Similarly, for 2.4 GHz frequency, the proposed circuit achieves a peak PCE of 26.5% at -6 dBm across a 1 MΩ load. The proposed RF-DC converter circuit shows a sensitivity of -20 dBm across a 1 MΩ load and produces a 1 V output DC voltage.

Examining the Determinants of Patient Perception of Physician Review Helpfulness across Different Disease Severities: A Machine Learning Approach.

(1) Background. Patients are increasingly using physician online reviews (PORs) to learn about the quality of care. Patients benefit from the use of PORs and physicians need to be aware of how this evaluation affects their treatment decisions. The current work aims to investigate the influence of critical quantitative and qualitative factors on physician review helpfulness (RH). (2) Methods. The data including 45,300 PORs across multiple disease types were scraped from Healthgrades.com. Grounded on the signaling theory, machine learning-based mixed methods approaches (i.e., text mining and econometric analyses) were performed to test study hypotheses and address the research questions. Machine learning algorithms were used to classify the data set with review- and service-related features through a confusion matrix. (3) Results. Regarding review-related signals, RH is primarily influenced by review readability, wordiness, and specific emotions (positive and negative). With regard to service-related signals, the results imply that service quality and popularity are critical to RH. Moreover, review wordiness, service quality, and popularity are better predictors for perceived RH for serious diseases than they are for mild diseases. (4) Conclusions. The findings of the empirical investigation suggest that platform designers should design a recommendation system that reduces search time and cognitive processing costs in order to assist patients in making their treatment decisions. This study also discloses the point that reviews and service-related signals influence physician RH. Using the machine learning-based sentic computing framework, the findings advance our understanding of the important role of discrete emotions in determining perceived RH. Moreover, the research also contributes by comparing the effects of different signals on perceived RH across different disease types.

A 0.617-2.7 GHz Highly Linear High-Power Dual Port 15 Throws Antenna Switch Module (DP15T-ASM) with Branched-Antenna Technique and Termination Mode.

This paper presents a Dual-Port-15-Throw (DP15T) antenna switch module (ASM) Radio Frequency (RF) switch implemented by a branched antenna technique which has a high linearity for wireless communications and various frequency bands, including a low- frequency band of 617-960 MHz, a mid-frequency band of 1.4-2.2 GHz, and a high-frequency band of 2.3-2.7 GHz. To obtain an acceptable Insertion Loss (IL) and provide a consistent input for each throw, a branched antenna technique is proposed that distributes a unified magnetic field at the inputs of the throws. The other role of the proposed antenna is to increase the inductance effects for the closer ports to the antenna pad in order to decrease IL at higher frequencies. The module is enhanced by two termination modes for each antenna path to terminate the antenna when the switch is not operating. The module is fabricated in the silicon-on-insulator CMOS process. The measurement results show a maximum IMD2 and IMD3 of -100 dBm, while for the second and third harmonics the maximum value is -89 dBc. The module operates with a maximum power handling of 35 dBm. Experimental results show a maximum IL of 0.34 and 0.92 dB and a minimum isolation of 49 dB and 35.5 dB at 0.617 GHz and 2.7 GHz frequencies, respectively. The module is implemented in a compact way to occupy an area of 0.74 mm2. The termination modes show a second harmonic of 75 dBc, which is desirable.

T/R RF Switch with 150 ns Switching Time and over 100 dBc IMD for Wideband Mobile Applications in Thick Oxide SOI Process.

This paper presents a fast-switching Transmit/Receive (T/R) Single-Pole-Double-Throw (SPDT) Radio Frequency (RF) switch. Thorough analyses have been conducted to choose the optimum number of stacks, transistor sizes, gate and body voltages, to satisfy the required specifications. This switch applies six stacks of series and shunt transistors as big as 3.9 mm/160 nm and 0.75 mm/160 nm, respectively. A negative charge pump and a voltage booster generate the negative and boosted control voltages to improve the harmonics and to keep Inter-Modulation Distortion (IMD) performance of the switch over 100 dBc. A Low Drop-Out (LDO) regulator limits the boosted voltage in Absolute Maximum Rating (AMR) conditions and improves the switch performance for Process, Voltage and Temperature (PVT) variations. To reduce the size, a dense custom-made capacitor consisting of different types of capacitors has been presented where they have been placed over each other in layout considering the Design Rule Checks (DRC) and applied in negative charge pump, voltage booster and LDO. This switch has been fabricated and tested in a 90 nm Silicon-on-Insulator (SOI) process. The second and third IMD for all specified blockers remain over 100 dBc and the switching time as fast as 150 ns has been achieved. The Insertion Loss (IL) and isolation at 2.7 GHz are -0.17 dB and -33 dB, respectively. This design consumes 145 uA from supply voltage range of 1.65 V to 1.95 V and occupies 440 × 472 µm2 of die area.

Serum progranulin is not associated with rs5848 polymorphism in Korean patients with neurodegenerative diseases.

Low serum progranulin (PGRN) is known to be associated with granulin (GRN) gene mutation and T alleles of GRN rs5848 polymorphism. However, there have been only a few Asian studies exploring these. We investigated the serum PGRN levels, rs5848 genotypes, and their relations with cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers in the Korean population. Serum PGRN levels, GRN rs5848 polymorphism, and GRN mutations were evaluated in 239 participants (22 cognitively unimpaired participants and 217 patients with neurodegenerative diseases). CSF AD biomarkers were also evaluated in 214 participants. There was no significant difference in the serum PGRN levels among the diagnostic groups. We could not find any GRN mutation carrier in our sample. The differences in the frequencies of the rs5848 genotypes among the clinical groups or the effects of the rs5848 genotypes on serum PGRN were not observed. There was no correlation between the serum PGRN level or rs5848 genotype and CSF AD biomarkers. Neither the T allele nor the TT genotype had an effect on the development of AD. Our results showed that serum PGRN levels were not associated with rs5848 genotypes, indicating that multiple single nucleotide polymorphisms might affect PGRN concentrations in an ethnicity-specific manner.

The Relationship of Plasma Transthyretin Level with Global or Regional Amyloid Beta Burden in Subjects with Amnestic Mild Cognitive Impairment: Cross-Sectional Amyloid PET Study.

Background: To investigate the relationships of plasma transthyretin levels with amyloid beta deposition and medial temporal atrophy in amnestic mild cognitive impairment. Methods: This is a cross-sectional study of association of subjects with amnestic mild cognitive impairment. Plasma transthyretin levels, brain magnetic resonance imaging, and 18F-florbetaben positron emission tomography were simultaneously measured in subjects with amnestic mild cognitive impairment. Results: Plasma transthyretin levels were positively associated with amyloid beta deposition in global (r = 0.394, P = .009), frontal cortex (r = 0.316, P = .039), parietal cortex (r = 0.346, P = .023), temporal cortex (r = 0.372, P = .014), occipital cortex (r = 0.310, P = .043), right posterior cingulate (r = 0.350, P = .021), left precuneus (r = 0.314, P = .040), and right precuneus (r = 0.398, P = .008). No association between plasma transthyretin level and medial temporal sub-regional atrophies was found. Conclusions: Our findings of positive association of plasma transthyretin levels with global and regional amyloid beta burden suggest upregulation of transthyretin level as a reactive response to amyloid beta deposition during the early stages of the Alzheimer's disease process.

A Low-Band Multi-Gain LNA Design for Diversity Receive Module with 1.2 dB NF.

This paper presents and discusses a Low-Band (LB) Low Noise Amplifier (LNA) design for a diversity receive module where the application is for multi-mode cellular handsets. The LB LNA covers the frequency range between 617 MHz to 960 MHz in 5 different frequency bands and a 5 Pole Single Throw (5PST) switch selects the different frequency bands where two of them are for the main and three for the auxiliary bands. The presented structure covers the gain modes from -12 to 18 dB with 6 dB gain steps where each gain mode has a different current consumption. In order to achieve the Noise Figure (NF) specifications in high gain modes, we have adopted a cascode Common-Source (CS) with inductive source degeneration structure for this design. To achieve the S11 parameters and current consumption specifications, the core and cascode transistors for high gain modes (18 dB, 12 dB, and 6 dB) and low gain modes (0 dB, -6 dB, and -12 dB) have been separated. Nevertheless, to keep the area low and keep the phase discontinuity within ±10∘, we have shared the degeneration and load inductors between two cores. To compensate the performance for Process, Voltage, and Temperature (PVT) variations, the structure applies a Low Drop-Out (LDO) regulator and a corner case voltage compensator. The design has been proceeded in a 65-nm RSB process design kit and the supply voltage is 1 V. For 18 dB and -12 dB gain modes as two examples, the NF, current consumption, and Input Third Order Intercept Point (IIP3) values are 1.2 dB and 16 dB, 10.8 mA and 1.2 mA, and -6 dBm and 8 dBm, respectively.

Mechanically Stable Kirigami Deformable Resonant Circuits for Wireless Vibration and Pressure Sensor Applications.

Deformable 3D structures have emerged to revolutionize next-generation flexible electronics. In this study, a large out-of-plane deformable kirigami-based structure integrated with traditional functional materials has been successfully applied to wirelessly sense mechanical vibration and pressure. Unlike spiral inductor coils that lack mechanical stability, the inductor coils supported with polymer kirigami designs, comprising concentric circles with alternately connected hinges among the consecutive layers, offer exceptional mechanical stability. The wireless sensor shows a good linear response (Adj. R2 = 0.99) between the shift in resonant frequency as a function of extension. Moreover, the sensor device exhibits excellent cycling mechanical stability and minimal hysteresis, as confirmed by the experiments performed for over 5 d. An acceleration sensor (0-20 ms-2) with high linearity (Adj. R2 = 0.99) is introduced. Furthermore, a highly sensitive low-pressure sensor is demonstrated wirelessly in real time. Thus, the sensor can wirelessly monitor mechanical vibration and pressure. It can be applied for motion tracking, health monitoring, soft robotics, and deformation detection in battery-free deformable electronic devices.

Examining Different Factors in Web-Based Patients' Decision-Making Process: Systematic Review on Digital Platforms for Clinical Decision Support System.

(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.

Exploring Readiness for Birth Control in Improving Women Health Status: Factors Influencing the Adoption of Modern Contraceptives Methods for Family Planning Practices.

BACKGROUND: Pakistan is the world's sixth most populated country, with a population of approximately 208 million people. Despite this, just 25% of legitimate couples say they have used modern contraceptive methods. A large body of literature has indicated that sexual satisfaction is a complex and multifaceted concept, since it involves physical and cultural components. The purpose of this study is to investigate the impact of influencing factors in terms of contraceptive self-efficacy (CSE), contraceptive knowledge, and spousal communication on the adoption of modern contraceptive methods for family planning (FP) under the moderating role of perceived barriers. METHODS: Data were collected using an adopted questionnaire issued to married women of reproductive age belonging to the Rawalpindi and Neelum Valley regions in Pakistan. The sample consisted of 250 married women of reproductive age. SPSS was used to analyze the respondents' feedback. RESULTS: The findings draw public attention towards CSE, contraceptive knowledge, and spousal communication, because these factors can increase the usage of modern methods for FP among couples, leading to a reduction in unwanted pregnancies and associated risks. Regarding the significant moderation effect of perceived barriers, if individuals (women) are highly motivated (CSE) to overcome perceived barriers by convincing their husbands to use contraceptives, the probability to adopt modern contraceptive methods for FP practices is increased. CONCLUSIONS: Policymakers should formulate strategies for the involvement of males by designing male-oriented FP program interventions and incorporating male FP workers to reduce communication barriers between couples. Future research should address several other important variables, such as the desire for additional child, myths/misconceptions, fear of side effects, and partner/friend discouragement, which also affect the adoption of modern contraceptive methods for FP practices.