OBJECTIVE: Our article demonstrates the effectiveness of using a validated framework to create a ChatGPT prompt that generates valid nursing care plan suggestions for one hypothetical older patient with lung cancer. METHOD: This study describes the methodology for creating ChatGPT prompts that generate consistent care plan suggestions and its application for a lung cancer case scenario. After entering a nursing assessment of the patient's condition into ChatGPT, we asked it to generate care plan suggestions. Subsequently, we assessed the quality of the care plans produced by ChatGPT. RESULTS: While not all the suggested care plan terms (11 out of 16) utilized standardized nursing terminology, the ChatGPT-generated care plan closely matched the gold standard in scope and nature, correctly prioritizing oxygenation and ventilation needs. CONCLUSION: Using a validated framework prompt to generate nursing care plan suggestions with ChatGPT demonstrates its potential value as a decision support tool for optimizing cancer care documentation.

OBJECTIVES: Electronic health records (EHRs) user interfaces (UI) designed for data entry can potentially impact the quality of patient information captured in the EHRs. This review identified and synthesized the literature evidence about the relationship of UI features in EHRs on data quality (DQ). MATERIALS AND METHODS: We performed an integrative review of research studies by conducting a structured search in 5 databases completed on October 10, 2022. We applied Whittemore & Knafl's methodology to identify literature, extract, and synthesize information, iteratively. We adapted Kmet et al appraisal tool for the quality assessment of the evidence. The research protocol was registered with PROSPERO (CRD42020203998). RESULTS: Eleven studies met the inclusion criteria. The relationship between 1 or more UI features and 1 or more DQ indicators was examined. UI features were classified into 4 categories: 3 types of data capture aids, and other methods of DQ assessment at the UI. The Weiskopf et al measures were used to assess DQ: completeness (n = 10), correctness (n = 10), and currency (n = 3). UI features such as mandatory fields, templates, and contextual autocomplete improved completeness or correctness or both. Measures of currency were scarce. DISCUSSION: The paucity of studies on UI features and DQ underscored the limited knowledge in this important area. The UI features examined had both positive and negative effects on DQ. Standardization of data entry and further development of automated algorithmic aids, including adaptive UIs, have great promise for improving DQ. Further research is essential to ensure data captured in our electronic systems are high quality and valid for use in clinical decision-making and other secondary analyses.

Current electronic health records (EHRs) are often ineffective in identifying patient priorities and care needs requiring nurses to search a large volume of text to find clinically meaningful information. Our study, part of a larger randomized controlled trial testing nursing care planning clinical decision support coded in standardized nursing languages, focuses on identifying format preferences after random assignment and interaction to 1 of 3 formats (text only, text+table, text+graph). Being assigned to the text+graph significantly increased the preference for graph (P = .02) relative to other groups. Being assigned to the text only (P = .06) and text+table (P = .35) was not significantly associated with preference for their assigned formats. Additionally, the preference for graphs was not significantly associated with understanding graph content (P = .19). Further studies are needed to enhance our understanding of how format preferences influence the use and processing of displayed information.

The aim of this article was to describe a novel methodology for transforming complex nursing care plan data into meaningful variables to assess the impact of nursing care. We extracted standardized care plan data for older adults from the electronic health records of 4 hospitals. We created a palliative care framework with 8 categories. A subset of the data was manually classified under the framework, which was then used to train random forest machine learning algorithms that performed automated classification. Two expert raters achieved a 78% agreement rate. Random forest classifiers trained using the expert consensus achieved accuracy (agreement with consensus) between 77% and 89%. The best classifier was utilized for the automated classification of the remaining data. Utilizing machine learning reduces the cost of transforming raw data into representative constructs that can be used in research and practice to understand the essence of nursing specialty care, such as palliative care.

OBJECTIVE: The study sought to present the findings of a systematic review of studies involving secondary analyses of data coded with standardized nursing terminologies (SNTs) retrieved from electronic health records (EHRs). MATERIALS AND METHODS: We identified studies that performed secondary analysis of SNT-coded nursing EHR data from PubMed, CINAHL, and Google Scholar. We screened 2570 unique records and identified 44 articles of interest. We extracted research questions, nursing terminologies, sample characteristics, variables, and statistical techniques used from these articles. An adapted STROBE (Strengthening The Reporting of OBservational Studies in Epidemiology) Statement checklist for observational studies was used for reproducibility assessment. RESULTS: Forty-four articles were identified. Their study foci were grouped into 3 categories: (1) potential uses of SNT-coded nursing data or challenges associated with this type of data (feasibility of standardizing nursing data), (2) analysis of SNT-coded nursing data to describe the characteristics of nursing care (characterization of nursing care), and (3) analysis of SNT-coded nursing data to understand the impact or effectiveness of nursing care (impact of nursing care). The analytical techniques varied including bivariate analysis, data mining, and predictive modeling. DISCUSSION: SNT-coded nursing data extracted from EHRs is useful in characterizing nursing practice and offers the potential for demonstrating its impact on patient outcomes. CONCLUSIONS: Our study provides evidence of the value of SNT-coded nursing data in EHRs. Future studies are needed to identify additional useful methods of analyzing SNT-coded nursing data and to combine nursing data with other data elements in EHRs to fully characterize the patient's health care experience.

With the rapid growth of electronic data repositories in diverse application domains, including healthcare, considerable research interest has been developed to solve issues related to extraction of hidden knowledge in these repositories. Electronic health record systems (EHRs) are the fastest growing in terms of size and data diversity. In this work, we focus on mining a high dimensional sparse dataset using nursing care data as an exemplar. To mine a high-dimensional and sparse dataset is a challenging task due to a number of reasons. There are several dimension reduction methods, however, they do not work well with contextual datasets. In our study, we have used association mining as a dimension reduction step and for extracting important features from the dataset. Our results show that association mining can be effectively used for dimension reduction and feature extraction step. Our predictive modeling results show that decision tree models generally have high accuracy and the results are easy to interpret and determine the influence of different variables.

In this paper, we propose a novel collision resolution scheme for random access in wireless sensor networks. If a collision incurs during fusion process, splitting algorithm is applied to resolve the collision dynamically and recursively, based on past channel states alone or channel states and sensing information together. The novelty of our splitting algorithm is two-fold: 1. we perform splitting based on the informativeness of sensor data, ensuring that more informative data will be collected first; 2. we optimize splitting intervals based on local summaries of sensors collected at each fusion step. As shown in our simulation results, the proposed schemes achieve significant channel and power efficiency gain, compared with fixed sample size test, traditional sequential probability ratio test, and Pseudo-Bayesian based contention protocol.

The problem of decentralized detection in a large wireless sensor network is considered. An adaptive decentralized detection scheme, group-ordered sequential probability ratio test (GO-SPRT), is proposed. This scheme groups sensors according to the informativeness of their data. Fusion center collects sensor data sequentially, starting from the most informative data and terminates the process when the target performance is reached. Wald's approximations are shown to be applicable even though the problem setting deviates from that of the traditional sequential probability ratio test (SPRT). To analyze the efficiency of GO-SPRT, the asymptotic equivalence between the average sample number of GO-SPRT, which is a function of a multinomial random variable, and a function of a normal random variable, is established. Closed-form approximations for the average sample number are then obtained. Compared with fixed sample size test and traditional SPRT, the proposed scheme achieves significant savings in the cost of data fusion.

Decentralized binary detection problem in ad-hoc sensor networks where a link between two sensors is on with a certain probability is considered in this paper. We propose a consensus based detection scheme where sensors exchange their local decisions, update their own decisions based on the exchanges and finally reach a consensus about the state of nature. We analyze the error probability and convergence of this decision consensus scheme. We show that with our scheme, the detection performance in ad-hoc networks is asymptotically equivalent to that of a parallel sensor network where all the local decisions are processed by a central node (fusion center) in the sense that the error exponents are the same. The probability distribution of the consensus time is also studied. Simulation and numerical results are given to verify the theoretical results.

In this work, we propose a contention-based protocol for general decentralized detection problem in the context of wireless sensor networks. In this scheme, fusion task is implemented in a multi-stage fashion: sensors are first grouped according to the informativeness of their data; fusion center then polls the sensor sets sequentially in the order of their informativeness until a target performance is reached. Within one stage, all polled sensors compete for a common channel medium where exists near-far effect, Raleigh fading, and shadowing. To determine the optimal transmission probability, we propose a novel Bayesian update algorithm utilizing both sensing information and channel feedback. The proposed dynamic protocol is applied to signal detection in Gaussian noise. As shown by our simulations, incorporating sensing information greatly improves efficiency over a generic Bayesian update scheme relying only on channel feedback. Our results also show that exploiting capture effect can significantly improve communication and energy efficiency. Comparison with fixed sample size test and sequential probability ratio test shows that the proposed scheme achieves significant efficiency gain over existing fusion strategies.

Improvements in transmitted video quality are achievable by utilizing multiple-input multiple-output (MIMO) systems for transmission of multiple description coded (MDC) video. For a particular MDC scheme, the improvements heavily depend on the selection of the underlying MIMO system. This paper proposes using a MIMO system with power control based antenna selection. Simulations show that the proposed system significantly outperforms the existing MDC/MIMO combinations in the literature and extends the range of channel signal-to-noise ratio (SNR) values for which video of given quality can be transmitted. Quality improvements result from MIMO power control which allows transmissions over available MIMO antennas only for sufficiently high channel gains (effectively preventing transmissions that result in video packet loss) and guarantees equal channel performance in terms of the bit error rate (BER) for all the balanced descriptions of equal importance.

We propose a novel standard-compliant multiple description coding (MDC) method that exploits the H.264/AVC redundant slice tool, performing rate-distortion optimization at the slice level. The strategy to allocate redundancy to each slice jointly takes into account its contribution to distortion, its position in the GOP, the effect of decoder error concealment, and the transmission conditions. This makes the algorithm more accurate with respect to previous frame-based solutions, and experimental results show that it compares favorably with other state-of-the-art standard-compliant MDC techniques.

A cross-layer method is proposed for optimizing, controlling and improving the quality of video transmission over wireless networks using scalable video coding (SVC) and multiple-input multiple-output (MIMO) transmission with channel state feedback (CSI). Multiple video sub-streams are created by a content-based partitioning and sorting of the enhancement layers produced with the SVC extension of H.264/AVC. Unlike in existing methods, the prioritized bit-streams are transmitted by actively performing power adjustment and antenna selection using a bit-stream prioritization matrix to modify the power allocation procedure of a recently proposed MIMO scheme. The power allocation strategy results in different bit error rate (BER) experienced by the bit-streams. Simulation results show an improved performance compared with power allocation that equalizes BER over the different channels.

The large peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) transmission translates to system performance degradation due to low power efficiency in the presence of nonlinear power amplification. To reduce PAPR, many OFDM systems have adopted the peak windowing method. However, existing peak windowing schemes suffer from performance limitations especially in the presence of multiple closely-spaced peaks. In this paper, two new peak windowing schemes are proposed to improve performance by taking care of closely-spaced peaks. Simulation results show that the two proposed schemes outperform existing schemes in attaining significantly lower out-of-band radiation given the same in-band distortion constraints.

In this work, we propose a random access protocol for decentralized detection in wireless sensor networks. In this scheme, sensors are grouped according to the informativeness of their data. Then fusion center collects sensor data sequentially in the order of their informativeness and terminates the fusion process once the target performance is reached. To determine the optimal transmission probability in random access, we propose a novel Bayesian update algorithm utilizing both the sensing information and the channel feedback. As shown by our simulations, incorporating sensing information greatly improves the communication efficiency over a generic Bayesian update scheme relying only on channel feedback. Comparison with fixed sample size test and sequential probability ratio test shows that the proposed scheme achieves significant channel and power efficiency gain over existing strategies.

Decentralized binary detection problem in ad hoc sensor networks with low inter-sensor communication rate is considered in this paper. We propose a consensus based detection scheme where sensors exchange their local decisions with their neighbors, update their own decisions based on the exchanges and finally reach a consensus about the state of nature. For analysis, we set up a Markov Chain model for this scheme. In the evaluation of the system performance, we focus on the asymptotic behavior of the detection error probability with respect to the number of sensors in the system. We show that with our scheme, the detection performance in ad hoc networks is asymptotically equivalent to that of a parallel sensor network where all the local decisions is processed by a central node (fusion center) in the sense that the error exponents are the same. Simulation and numerical results are given at the end of this paper to verify the theoretical results.

We consider the problem of distributed detection in a large wireless sensor network. An adaptive data fusion scheme, group-ordered sequential probability ratio test (GO-SPRT), is proposed. This scheme groups sensors according to the informativeness of their data. Fusion center collects sensor data sequentially, starting from the most informative data and terminates the process when the target performance is reached. To analyze the average sample number, we establish the asymptotic equivalence between GO-SPRT, a multinomial experiment, and a normal experiment. Closed-form approximates are obtained. Our analysis and simulations show that, compared with fixed sample size test and traditional sequential probability ratio test (SPRT), the proposed scheme achieves significant savings in the cost of data fusion.

Many applications of wireless sensor networks require that the sensor nodes be location-aware. Using RSS, TOA, TDOA and other range measurement techniques, we are able to get the measurements of dissimilarity or pairwise distance between different neighboring sensor nodes. In this paper we use these pairwise distances to make local maps, and then align the overlapping local maps to acquire relative global coordinates of sensor nodes. We present local tangent space alignment (LTSA)-based localization, and local MDS based localization (LMBL) schemes owing to development of manifold learning algothrims. Simulations show that LMBL and LTSA-based scheme outperform the LLE-based scheme.

While antenna selection has been shown to be effective in improving bit error rate (BER) performance in multiple-input-multiple-output (MIMO) systems, further performance gain can be obtained by transmitting only when the channel gains are sufficiently high and by using power control to compensate for channel variations. Truncated channel inversion is an adaptive power control scheme that compensates for fading above a certain cutoff fade depth: below the cutoff level the transmission is stopped. In this paper, we propose a novel truncated channel inversion power control based antenna selection scheme to improve the BER performance of spatial multiplexing (SM) systems with linear receivers. Power control based optimal and sub-optimal antenna selection criteria are proposed to dynamically select the number of active transmit antennas and the mapping of data substreams to transmit antennas. Simulations show that, in flat Rayleigh fading channels, the proposed power control based antenna selection scheme significantly outperforms the existing schemes in the literature.

Spatial multiplexing (SM) proves effective in increasing data rate in a narrowband multiple-input multiple-output (MIMO) system. Past work has shown that unitary precoding with limited feedback can be used at the transmitter to improve the bit error rate (BER) performance. Further performance gain can be achieved if power control is used along with unitary preceding at the transmitter. In this paper we propose a novel power control scheme called modified spatial-temporal truncated channel inversion (MST-TCI) for limited feedback MIMO systems in block Rayleigh fading channels. A low-complexity algorithm called codebook optimum index loading (COIL) is proposed to generate near-optimum codebooks for quantizing MST-TCI power control information. Simulation results show that the proposed scheme significantly outperforms the limited feedback unitary precoding scheme

User cooperation can provide spatial transmit diversity gains, enhance coverage and potentially increase capacity. Existing works have focused on two-user cooperative systems with perfect channel state information at the receivers. In this paper, we develop several distributed space-time processing schemes for general N-user cooperative systems, which do not require channel state information at either relays or destination. We prove that full spatial diversity gain can be achieved in such systems. Simulations demonstrate that these cooperative schemes achieve significant performance gain

User cooperation can provide spatial transmit diversity gains, enhance coverage and potentially increase capacity. Existing works have focused on two-user cooperative systems with perfect channel state information at the receivers. In this paper, we develop several distributed space-time processing schemes for general TV-user cooperative systems, which do not require channel state information at either relays or destination. We prove that full spatial diversity gain can be achieved in such systems. Simulations demonstrate that these cooperative schemes achieve significant performance gain.

We consider the problem of minimizing the energy needed for data fusion in a large scale sensor network by varying the transmission times assigned to different sensor nodes. The optimal scheduling protocol is derived, based on which, we develop a low-complexity inverse-log scheduling algorithm that achieves near-optimal energy efficiency. To eliminate the communication overhead required by centralized scheduling protocols, we also develop a distributed inverse-log protocol that is applicable to networks with a large number of nodes. Simulations demonstrate that this distributed scheduling protocol achieves substantial energy savings over uniform time division multiple access protocol.

Relayed transmissions enable low-power communications among nodes (possibly separated by a large distance) in wireless networks. Since the capacity of general relay channels is unknown, we investigate the achievable rates of relayed transmissions over fading channels for two transmission schemes: the block Markov coded and the time-division multiplexed (TDM) transmissions. The normalized achievable minimum energy per bit required for reliable communications is derived, which also enables optimal power allocation between the source and the relay. The time-sharing factor in TDM transmissions is optimized to improve achievable rates. The region where relayed transmission can provide a lower minimum energy per bit than direct transmission, as well as the optimal relay placement for these two transmission schemes, are also investigated. Numerical results delineate the advantages of relayed, relative to direct, transmissions.

Relying on a kurtosis-type criterion, we develop a low-complexity blind carrier frequency offset (CFO) estimator for orthogonal frequency-division multiplexing (OFDM) systems. We demonstrate analytically how identifiability and performance of this blind CFO estimator depend on the channel's frequency selectivity and the input distribution. We show that this approach can be applied to blind CFO estimation in multi-input multi-output and multiuser OFDM systems. The issues of channel nulls, multiuser interference, and effects of multiple antennas are addressed analytically, and tested via simulations.

We consider the problem of minimizing the energy needed for data fusion in a sensor network by varying the transmission times assigned to different sensor nodes. The optimal scheduling protocol is derived, based on which we develop a low-complexity inverse-log scheduling (ILS) algorithm that achieves near-optimal energy efficiency. To eliminate the communication overhead required by centralized scheduling protocols, we further derive a distributed inverse-log protocol that is applicable to networks with a large number of nodes. Focusing on large-scale networks with high total data rates, we analyze the energy consumption of the ILS. Our analysis reveals how its energy gain over traditional time-division multiple access depends on the channel and the data-length variations among different nodes.

Relay links are expected to play a critical role in the design of wireless networks. This paper investigates the energy efficiency of relay communications in the low-power regime under two different scenarios: when the relay has unlimited power supply and when it has limited power supply. A system with a source node, a destination node, and a single relay operating in the time division duplex (TDD) mode was considered. Analysis and simulations are used to compare the energy required for transmitting one information bit in three different relay schemes: amplify and forward (AnF), decode and forward (DnF), and block Markov coding (BMC). Relative merits of these relay schemes in comparison with direct transmissions (direct Tx) are discussed. The optimal allocation of power and transmission time between source and relay is also studied.

Power loading algorithms improve the data rates of orthogonal frequency division multiplexing (OFDM) systems. However, they require the transmitter to have perfect channel state information, which is impossible in most wireless systems. We investigate the effects of imperfect (and thus partial) channel feedback on the throughput of OFDM systems. Two channel uncertainty models are studied: 1) the ergodic model, where average rate is the figure of merit and 2) the quasi-static model, where outage rate is relevant. Rate-power allocation algorithms are developed. The throughput achieved by these algorithms and the effects of channel multipath are investigated analytically and with simulations.

We investigate the information regularity and identifiability of the blind source separation problem with constant modulus constraints on the sources. We demonstrate that the information regularity (existence of a finite Cramer-Rao bound) is closely related to local identifiability. Sufficient and necessary conditions for local identifiability are derived. We also study the conditions under which unique (global) identifiability is guaranteed within the inherently unresolvable ambiguities on phase rotation and source permutation. Both sufficient and necessary conditions are obtained.

Relay links are expected to play a critical role in the design of wireless networks. In this paper, we investigate the energy efficiency of relay communications under two different scenarios: when the relay has unlimited and when it has limited power supply. Relative merits of these relay schemes in comparison with direct transmissions are discussed.

The performance of linear minimum mean-square error (MMSE) multiuser receivers in a dual-rate synchronous direct-sequence code-division multiple-access (DS-CDMA) system is investigated using the random spreading sequence analysis. Multicode (MC) systems and different variants of variable spreading length (VSL) systems are studied. User capacity regions are obtained for these systems.

Power loading algorithms improve the data rates of OFDM systems. However, they require the transmitter to have perfect channel state information, which is impossible in most wireless systems. We investigate the effects of imperfect (and thus partial) channel feedback on the achievable rates of OFDM systems. Two cases are studied: i) ergodic channels, where average rate is the figure of merit; and ii) quasistatic channels, where the outage rate is relevant. Power loading algorithms and the effects of channel multipath are investigated analytically and with simulations.

This letter considers multiuser detection in variable-spreading-length multi-rate direct-sequence code-division multiple-access systems. A two-layer spreading (TLS) code scheme is proposed which facilitates the low-complexity adaptive implementation of linear minimum mean-square error multiuser receivers in a dual-rate system. It is demonstrated via large system analysis and simulation that imposing a TLS structure does not incur loss in terms of the output signal-to-interference ratio performance.

The performance of linear MMSE multiuser receivers in a dual-rate synchronous DS-CDMA system is investigated using the effective interference and effective bandwidth methodology first proposed by Tse and Hanly (see IEEE Trans. Inform. Theory, vol.45, no.2, 1999). Both multi-code (MC) systems and variable spreading length (VSL) systems are studied. User capacity regions are obtained for these systems.