The health care system encompasses the participation of individuals, groups, agencies, and resources that offer services to address the requirements of the person, community, and population in terms of health. Parallel to the rising debates on the healthcare systems in relation to diseases, treatments, interventions, medication, and clinical practice guidelines, the world is currently discussing the healthcare industry, technology perspectives, and healthcare costs. To gain a comprehensive understanding of the healthcare systems research paradigm, we offered a novel contextual topic modeling approach that links up the CombinedTM model with our healthcare Bert to discover the contextual topics in the domain of healthcare. This research work discovered 60 contextual topics among them fifteen topics are the hottest which include smart medical monitoring systems, causes, and effects of stress and anxiety, and healthcare cost estimation and twelve topics are the coldest. Moreover, thirty-three topics are showing in-significant trends. We further investigated various clusters and correlations among the topics exploring inter-topic distance maps which add depth to the understanding of the research structure of this scientific domain. The current study enhances the prior topic modeling methodologies that examine the healthcare literature from a particular disciplinary perspective. It further extends the existing topic modeling approaches that do not incorporate contextual information in the topic discovery process adding contextual information by creating sentence embedding vectors through transformers-based models. We also utilized corpus tuning, the mean pooling technique, and the hugging face tool. Our method gives a higher coherence score as compared to the state-of-the-art models (LSA, LDA, and Ber Topic).

Healthcare systems are intricate. These are composed of various interrelated parts. The World Health Organization (WHO) says a health system is “all institutions, people, and actions whose primary aim is to promote, restore, or maintain health.” They usually include both rural and urban areas, public and private systems, formal/allopathic, and informal/traditional methods of providing healthcare, as well as being at the national level, giving them a tremendous scope. [1].

There are many more functions that health systems do in society in addition to providing healthcare and other treatments to maintain or improve health. They aid in preventing financial fallout from illness and medical costs for homes. It's imperative to keep in mind that health systems contribute to society's economy [2]. The health system is a sector of the economy that provides employment, revenue, and business opportunities for many health workers and enterprises. For instance, there is some research that suggests that a population's health may have an impact on economic productivity. A wider range of societal norms and values are established through health systems, which are also social and cultural institutions [3]. Health systems and the broader environment interact dynamically. Because of their diffuse nature and usually porous borders, health systems must include the social, political, and economic environment while assessing their structure and efficacy.

Moreover, the health systems are locations where actors with various wants and desires compete and argue. Setting health priorities, funding health systems, and allocating resources within the system are all contentious issues. The place of the state and the market within health systems, as well as the function that a health system should serve in society, are frequently the subject of ideological and political disagreement. These various facets of the complexity of health systems are rarely addressed simultaneously and are transdisciplinary. The fact is that numerous disciplinary perspectives, such as those of history, economics, medicine, epidemiology, politics, law, ethics, anthropology, and sociology, are necessary to fully study and comprehend health systems [1].

The researchers in different studies applied bibliometric methods to analyze the healthcare literature [4, 5] or extract latent patterns from the scientific literature on various subjects of the healthcare system [6, 7, 8]. For example, in [6] authors describe a mapping of the research on healthcare operations and supply chain management. In [8] scholars state the exploratory analysis of research on IoT in healthcare. These studies are limited to a specific disciplinary viewpoint and do not show the holistic picture of healthcare research. Moreover, these approaches do not capture the context of the discussion. In the current study through the application of computational methods and advanced topic modeling tools, we capture the context of the research so that the topics are more semantically understandable. This goal is achieved by utilizing a contextual word embedding-based topic modeling method. It uses sentences as the elementary unit of analysis for creating embeddings. Combining computational methods with qualitative data analysis, we provided highly objective, coherent, superior, and meta-analytical insight into current research on healthcare systems. This study's overall technical and theoretical contributions can be illustrated as follows:

  • We developed a novel contextual topic modeling approach that incorporates corpus tuning and mean pooling techniques to design healthcare Bert which we link up with CMT(CombinedTM) to generate the contextual topic modeling in the domain of healthcare care.

  • We compare our model with LSA, LDA, and Ber Topic. Our model achieves a maximum coherence score as compared to these state-of-the-art models.

  • This study performs the quantitative assessment of scientific literature in the domain of health care in terms of contextual topics and classified the topics into hot and cold categories on the basis of p-values. We also investigated topic clusters and correlations by exploring inter-topic distance maps.

  • We also elaborated the top cited studies qualitatively to cross-validate the topic themes which enhance the significance of our findings.

The rest of the article is divided into six sections. The first section describes the introduction. The related work is covered in the second section, and the materials and methods are covered in depth in the third section. Results and discussions are covered in the fourth section, followed by a conclusion in the fifth section.

Here, we discuss (i) the top-cited works on the healthcare system as well as (ii) related work on topic modeling approaches.

2.1 Healthcare Systems

This section covers some overviews of highly cited research on healthcare systems. Significant research generally deals with diseases, diagnoses, interventions, treatments, and other related subjects. In the context of diseases, the scholars focus on heart disease stroke, congenital heart disease, and other vascular diseases [9,10,11,12]. Regarding healthcare interventions, scholars emphasize various subjects such as the acceptability of the interventions, evaluation of interventions, behavioral interventions, and care transition interventions [13, 14]. The most cited research also analyzes medication effectiveness [15] and stigma as the cause of health inequality, service utilization of lifetime mental disorder, cultural competence in the delivery of healthcare services, and patient perception of hospital care [16].

The healthcare industry is undoubtedly the most significant of all the sectors that have profited from technological adoption. As a result, it eventually raised the standard of living and contributed to several life savings. The research scholars developed various tools and techniques to automate the various operations and tasks from a healthcare perspective. For example, a 3D slicer, a clinical research tool similar to a radiology workstation [17]. WSN technologies have various applications in the health sector like sensor-integrated devices which can monitor human activity such as pressure, temperature, and strain. It also provides monitoring facilities through contextual information that minimizes the caregiver's needs [18, 19]. Additionally, the IoT-enabling solutions based on a WSN, RFID, and mobile technology can monitor patients, personnel, and healthcare devices are another application of technology for healthcare services [20].

Material sciences also have a significant contribution to the healthcare industry. For example, electrospun nanofibers can be used for membrane preparation [21]. Silk-molded electronic skin can monitor the psychological signals of human beings [22]. From a healthcare perspective, a large amount of data is generated that can be processed using machine learning and deep learning techniques. It advances healthcare research and improves human health [23,24]. Beyond these areas, the physician acceptance of telemedicine technology and cell phone-based interventions (voice, text messages) are being evaluated as an alternative to ordinary business settings [25,26]. Additionally, the usage of technology in the healthcare system positively impacts hospital revenue and the quality of services delivered to patients [27]. Video conferencing technology has also been applied in healthcare to train primary care providers to treat complex diseases like HCV infection, which increase the patient-treatment ratio [28].

Regarding healthcare costs and related subjects, researchers pointed out various factors that increase or decrease healthcare costs. For example, medication adherence, higher medical adherence decreases patient hospitalization [29, 30]. The implication of patient follow-up intervention [31] and fall-related injuries can overcome re-hospitalization risk and expenditure [32,33,34]. Surgical site infections (SSIs) are one of the major contributors to healthcare-associated infections and contribute significantly to the damage in medical care through the over-length of stay at hospitals [35, 36]. Infections such as; Clostridium difficile infection and antibiotic-resistant bacteria threaten the healthcare system as these are the cause of various deaths [37,38,39,40]. Therefore, the deployment of a surveillance system can provide an estimate of the burden of the infection. Additionally, prevention activities along with surveillance can avoid infections and overcome the burden of extra costs in the healthcare system [41]. Cancer imposes various health and economic burdens in terms of its treatment which reflects a substantial increase that highlights the importance of cancer prevention efforts, which may result in future savings to the healthcare system. Therefore, research recommends early cancer detection and treatment for effective cancer control [42,43].

The most cited themes also highlight the clinical practice guidelines frequently used as recommendations [44] such as guidelines related to diagnosis, prevention, intervention, treatment, and patient safety or care [4554].

2.2 Topic Modeling

The group of algorithmic machine learning techniques used in the field of text mining is called probabilistic topic models. These models look for structural patterns within a corpus to extract semantic data. The topic templates create word clusters representing the major subjects in a given corpus. These methods offer an automatic method of locating common topics in the papers that are being displayed in this manner. Topic modeling can be performed using various approaches that employ algorithms like NMF, LSA LDA, and clustering employing the K-means or Ward's method used for hierarchical clustering [55,56].

A variety of statistical and probabilistic approaches are used in language modeling (LM) to estimate the likelihood that a given string of words will appear in a sentence. Language models examine the corpora of text data to provide a foundation for their word predictions. These models are more efficient as compared to other approaches as they take into account the meaning and semantics of words and sentences as well as the relationship between words. Additionally, by using this strategy, we were able to achieve the highest level of semantic integrity within each topic, enhancing the topics’ relevance and differentiating them from one another [57]. Moreover, google created a transformer-based machine learning method for pre-training natural language processing called Bidirectional Encoder Representations from Transformers Known as BERT. It was developed and released by Google employees Jacob Devlin and his team in 2018 [58].

In the literature, we found various approaches that focused on the analysis of scientific discourses. The existing studies mostly concentrated on using bibliometric techniques [4, 5], Latent Dirichlet Allocation (LDA) based topic modeling techniques [56, 59] or qualitative content analysis [60]. The other qualitative methods also applied by the researchers in the scientific trajectory analysis include systematic mapping reviews, critical reviews, and narrative reviews [61]. Researchers primarily developed keywords-based analysis techniques that do not usually capture the context of the discussion. The applications of traditional methods such as Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis are difficult given the high dimensionality of massive data. The problem also exists of unclear topics caused by the sparse distribution of topics [62]. In [63] Saheb et al. (2022) propose a context-based topic modeling approach. It integrates the general Bert model LDA and K-means clustering to contextually analyze research articles. The generalized Bert model is less efficient in detecting cluster quality and Kmeans is inefficient in generating clusters of data which has outliers. To solve the above challenges, we applied contextual topic modeling [64] in the domain of health care. We tuned the specialized Bert model (Medical-Bio-Bert2) on the corpus of healthcare research articles and then added a mean pooling layer on top of it giving us a novel HealthcareBert which we link with the CombinedTM (CTM) model to develop a novel contextual topic model. This strategy improves the coherence score, giving more accurate embeddings and resulting contextualized topics. We also compared our model with the state of art LSA, LDA, and Ber Topic models. Our model outperforms the existing models. We further investigated the topic trends and correlations among the contextualized topics to add more dimension to understanding the healthcare landscape.

In this section, we discuss the data collection, its pre-processing, and the methodology for the contextual topic modeling procedure in detail.

3.1 Data Collection

We selected Scopus as a data source and the following query: TITLE-ABS-KEY ((healthcare system* OR health care system* OR Health-care system* OR healthcare* OR health care* OR Health-care*)) AND (LIMIT-TO (SRCTYPE, “j”)) AND LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)) is executed in this database on 30th November 2022. As a result, we obtained 29600 records having publication dates from 2000 to 2022. We removed duplicates and empty abstract articles from the dataset; the rest have 28036 records.

3.2 Pre-processing

After the data collection process was finished, pre-processing of the data was done before modeling to increase the data quality. The text in the data set was first divided into tokens using word tokenization. Then lowercase was applied to the tokens. The text was cleaned up by getting rid of the numerals, punctuation, and stop words. This was achieved using a typical English stop word list (n=153). The text is cleaned up using the stemming and lemmatization procedures.

3.3 Healthcare Bert

We develop a transformer-based deep learning model as Healthcare Bert to enhance the semantic understanding of the topics. We tuned fspanda-Medical-Bio-Bert2 available through the hugging faces tool on the healthcare corpus and generate an improved Transformers-based model to provide more accurate context vectors in contextual topic modeling. There are various pooling methods (e.g., CLS pooling, Mean pooling, Max pooling) for transformer models [65]. We added these three layers on top of HealthcareBert one by one and computed the coherence score through the CMT model. However, the addition of mean pooling layer gives a higher coherence score so we added this layer on top of our HealthcareBert. (See algorithm-1)

3.4 Topic Models and CombinedTM

Latent Dirichlet allocation (LDA) is an important and widely used probabilistic topic model. It is based on a generative process denoted by the equation as follows:

p(θ,z,w|α,β)=p(θ|α)n=1Np(zn|θ)p(wn|zn,β)
(i)

Since Dirichlet prior is not a location-scale family, to solve this issue in ProdLDA the decoder network is used to approximate the Dirichlet prior p(θ|α) with a logistic normal distribution given by the equation where μ and Σ represents the outputs of the encoder network as follows:

p(θ|α)p(θ|μ,Σ)=LN(θ|μ,Σ)
(ii)

The encoder network has some disadvantages that it is stuck in a bad local optimum this problem is addressed using Adam optimizer, batch normalization, and dropout units in the encoder network. Another difference between LDA and ProdLDA is that β is unnormalized and wn is defined as:

(ωn|β,θCategorial(σ(βθ))
(iii)

CombinedTM (CTM) is a contextualized topic model inspired by ProdLDA even though both of the models use the same hyperparameters. The original CTM model uses SBERT features in combination with the Bow (Bag of words). In the current study, we replaced SBERT with a novel Healthcare BERT. We fetched and processed the data set using Panda's data frame. The CombinedTM model is employed that integrates [66] contextualized embeddings and a bag of words model. Finally, contextual topics were generated through CombinedTM, and the sentence embedding vectors were constructed using the improved healthcare Bert we have developed in the current study as shown in (Fig. 1).

Figure 1.

Contextual topic modeling framework.

Figure 1.

Contextual topic modeling framework.

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3.5 Model Evaluation

For the assessment of the validity of our approach, we design the following criteria (i) quantitative assessment (Coherence score) and (ii) qualitative assessment (highly cited literature).

(i) Quantitative assessment (Coherence Score)

The selection of an optimal model from the list of generated models is a critical task. Human comprehension depends on the concept of semantic context, and the coherence method makes an attempt to determine the context between words in a topic. Maximising the coherence score is crucial because it provides subjects that are easier for humans to understand. This context cannot be captured by the other matrices (such as perplexity). As a result, we assess the model's performance using the coherence score [67]. We generated various models with no. of topics k in the range of (10 to 100 with the step of 5) employing healthcare Bert with CombinedTM and plot the coherence score as (Coherence Cv) corresponding to each model in both cases. We choose the CombinedTM model that gives the maximum value of coherence Cv among the lists of generated models.

Coherence(V)=(vi,vj)εVscore(vi,vj,o`)
(iv)

(ii) Qualitative assessment

For a thematic understanding of the discovered topics, we also adopted a qualitative approach to cross-validate the topic's themes. In this method, we summarize the highly cited literature in section (2.1) which facilitates the general assessment of topic themes.

3.6 Topic Trends and Popularity Measurement

In this study, every topic's trends are examined, and the posterior distribution is linked to the year that each document was published. Each document gives a certain topic to it that best represents its likelihood at that current time. By dividing the total number of papers each year by the number of papers assigned to this topic, the total number of papers was normalized to determine the topic proportion for each topic each year. Following that, the cumulative proportion was used to calculate the overall popularity. Additionally, the Mann-Kendall trend test (M-K-test) is used to look for enduring upward or downward patterns in the data gathered over time. It is a non-parametric trend test method that examines discrepancies between earlier and later data points and is applicable to all distributions. It denotes that when a trend is present, sign values consistently tend to rise or fall [68]. Each topic's rising and declining patterns were recorded using the Mann-Kendall test.

3.7 inter-Topic Distances and Topic Correlations

We used LDAvis, a visualizing tool for topic models, to aid interpretations of the contextualized topics. This package plots the topics on a two-dimensional plane which gives us inter-topic distances, topic clusters, and resulting correlations.

In this study firstly we develop a transformer-based deep learning model to enhance the semantic understanding of the topics. Google created various transformer-based machine learning methods for pre-training natural language processing which required large computational resources. So, we search first for the most suitable transformer-based model existing on the hugging face (fspanda-Medical-Bio-BERT2) that can provide word embeddings for our data set. To improve these word embeddings, we tuned this model further using Google GPU and Colab Notebook on the local corpus. Further, we added a mean pooling layer after corpus tuning. In this way, we generated a novel model as Healthcare Bert. These word embeddings are provided as context vectors to the CTM model that combine a bag of words and word embeddings to generate contextual topics of the corpus. The newly generated word embeddings also improve the topic's coherence score of the CTM models. CTM generates a document term matrix (DTM) which we further investigated using various techniques of statistics to classify the topics into hot and cold categories. Five subheadings—(i) models’ evaluation/performance analysis, (ii) contextual topics, (iii) classification of topic trends, and (v) inter-topic distances topic clusters, and correlations are used to organize the study's findings.

4.1 Models’ Evaluation/Performance Analysis

We programmed coherence matric (C_v) to compare the proposed model (CMT) with LSA, LDA, and Ber Topic. Table 1 shows the values of the coherence score (C_v) and no. of topics (K) applying our healthcare Bert with CTM and Ber Topic. We also computed the coherence score of other state-of-the-art methods (LSA and LDA) on our data set without our healthcare Bert. All values of the coherence score computed using different models are analyzed and the CTM model generated using our novel healthcare Bert with k=60 gives a clear maxima value and is chosen as an optimal model to report the contextualized topics as shown in (See Table 1).

Table 1.
Performance Analysis of LSA, LDA, Ber Topic, and Our Method.
Topic NoLSALDABer TopicOur Method
10 0.3516 0.4176 0.4212 0.5880 
15 0.3363 0.4652 0.4183 0.5841 
20 0.3210 0.4592 0.3852 0.5850 
25 0.3142 0.4684 0.3781 0.5898 
30 0.3119 0.4860 0.3539 0.5831 
35 0.3078 0.4873 0.3554 0.5775 
40 0.2976 0.4778 0.3482 0.5840 
45 0.2932 0.4979 0.3375 0.5873 
50 0.2912 0.4763 0.3373 0.5806 
55 0.2849 0.4686 0.3414 0.5848 
60 0.2827 0.4662 0.3272 0.5978 
65 0.2747 0.4754 0.3370 0.5782 
70 0.2739 0.4637 0.3297 0.5817 
75 0.2674 0.4802 0.3251 0.5875 
80 0.2676 0.4399 0.3205 0.5819 
85 0.2650 0.4638 0.3294 0.5517 
90 0.2601 0.4300 0.3342 0.5608 
95 0.2562 0.4384 0.3309 0.5771 
100 0.2563 0.4472 0.3332 0.5657 
Topic NoLSALDABer TopicOur Method
10 0.3516 0.4176 0.4212 0.5880 
15 0.3363 0.4652 0.4183 0.5841 
20 0.3210 0.4592 0.3852 0.5850 
25 0.3142 0.4684 0.3781 0.5898 
30 0.3119 0.4860 0.3539 0.5831 
35 0.3078 0.4873 0.3554 0.5775 
40 0.2976 0.4778 0.3482 0.5840 
45 0.2932 0.4979 0.3375 0.5873 
50 0.2912 0.4763 0.3373 0.5806 
55 0.2849 0.4686 0.3414 0.5848 
60 0.2827 0.4662 0.3272 0.5978 
65 0.2747 0.4754 0.3370 0.5782 
70 0.2739 0.4637 0.3297 0.5817 
75 0.2674 0.4802 0.3251 0.5875 
80 0.2676 0.4399 0.3205 0.5819 
85 0.2650 0.4638 0.3294 0.5517 
90 0.2601 0.4300 0.3342 0.5608 
95 0.2562 0.4384 0.3309 0.5771 
100 0.2563 0.4472 0.3332 0.5657 

4.2 Context-Based Topics

This section lists the contextualized topics that our innovative healthcare Bert and the CTM model uncovered. The terms of each topic are supplied in ( Appendix A) and explained in (See Table 2).

Table 2.
Top 20 terms-based topic descriptions.
Topic descriptionTrendsTopic descriptionTrends
01: 95 Ci for transplantation ↑↑ 31: Asthma and costs ↓↓↓↓ 
02: 95 Ci testing ↑↑↑↑ 32: Drug management and treatment ↑ 
03: Patient treatment cost analysis ↓ 33: Hypertension diabetes and medication ↓ 
04: Cost gained strategy ↓↓ 34: Global financing for public health ↑↑ 
05: Diabetes Intervention ↓ 35: Technology adoption and its acceptance ↑ 
06: Health expenditure ↓ 36: Nurses’ job satisfaction ↓ 
07: Resistance in MRSA isolation and transmission ↓↓↓↓ 37: Healthcare IoT ↑ 
08: Healthcare services ↑↑ 38: Surgery and Complications ↑↑↑↑ 
09: Costs estimation ↑↑↑ 39: Role of nursing as a healthcare professional ↓↓↓ 
10: 95 Ci for patient disparities ↑↑ 40: Management of organizational processes ↓ 
11: Antimicrobial compliance ↓↓ 41: Side effects of overwork ↓ 
12: 95 Ci hospital risks ↑↑↑↑ 42: Smart medical monitoring system ↑↑ 
13: Patient treatment risks ↑↑↑↑ 43: Coronavirus Investigation ↑ 
14: Home intervention for patient care ↓↓↓↓ 44: Patient care quality and healthcare ↑↑ 
15: Brest cancer ↓ 45: Care quality and management ↓↓↓↓ 
16: Hygiene compliance ↓↓↓ 46: Needs of cancer patients ↓ 
17: Glucose control intervention ↓↓ 47: Research on health policy ↑↑↑↑ 
18: Maternal inequalities ↑ 48: Patient care quality ↓ 
19: Children's hospitalization rates ↓ 49: Sexual substance and discrimination ↓ 
20: Healthcare Education ↓ 50: Healthcare quality indicators ↓ 
21: HPV vaccination and immunization ↓ 51: Healthcare devices ↑ 
22: Health pandemic and vaccination ↑↑ 52: Women's stigma ↓ 
23: Reviews of healthcare research ↑↑ 53: Healthcare Discrimination ↓ 
24: Infection-associated cost ↓ 54: Telemedicine technology ↓↓↓↓ 
25: Pneumonia patients ↓ 55: Physical and emotional effects on quality of life (QOL) ↓↓ 
26: Primary healthcare services ↑ 56: Nursing and ethical policy ↓ 
27: Patient healthcare cost and services ↓ 57: Consumer documentation ↓ 
28: Asthma ↓↓ 58: Childhood disparities ↑ 
29: Hospital charges and costs ↓ 59: Audit attendance category ↓ 
30: Causes and effects of anxiety and stress ↑↑↑ 60: Inappropriate audit attendance ↓ 
Topic descriptionTrendsTopic descriptionTrends
01: 95 Ci for transplantation ↑↑ 31: Asthma and costs ↓↓↓↓ 
02: 95 Ci testing ↑↑↑↑ 32: Drug management and treatment ↑ 
03: Patient treatment cost analysis ↓ 33: Hypertension diabetes and medication ↓ 
04: Cost gained strategy ↓↓ 34: Global financing for public health ↑↑ 
05: Diabetes Intervention ↓ 35: Technology adoption and its acceptance ↑ 
06: Health expenditure ↓ 36: Nurses’ job satisfaction ↓ 
07: Resistance in MRSA isolation and transmission ↓↓↓↓ 37: Healthcare IoT ↑ 
08: Healthcare services ↑↑ 38: Surgery and Complications ↑↑↑↑ 
09: Costs estimation ↑↑↑ 39: Role of nursing as a healthcare professional ↓↓↓ 
10: 95 Ci for patient disparities ↑↑ 40: Management of organizational processes ↓ 
11: Antimicrobial compliance ↓↓ 41: Side effects of overwork ↓ 
12: 95 Ci hospital risks ↑↑↑↑ 42: Smart medical monitoring system ↑↑ 
13: Patient treatment risks ↑↑↑↑ 43: Coronavirus Investigation ↑ 
14: Home intervention for patient care ↓↓↓↓ 44: Patient care quality and healthcare ↑↑ 
15: Brest cancer ↓ 45: Care quality and management ↓↓↓↓ 
16: Hygiene compliance ↓↓↓ 46: Needs of cancer patients ↓ 
17: Glucose control intervention ↓↓ 47: Research on health policy ↑↑↑↑ 
18: Maternal inequalities ↑ 48: Patient care quality ↓ 
19: Children's hospitalization rates ↓ 49: Sexual substance and discrimination ↓ 
20: Healthcare Education ↓ 50: Healthcare quality indicators ↓ 
21: HPV vaccination and immunization ↓ 51: Healthcare devices ↑ 
22: Health pandemic and vaccination ↑↑ 52: Women's stigma ↓ 
23: Reviews of healthcare research ↑↑ 53: Healthcare Discrimination ↓ 
24: Infection-associated cost ↓ 54: Telemedicine technology ↓↓↓↓ 
25: Pneumonia patients ↓ 55: Physical and emotional effects on quality of life (QOL) ↓↓ 
26: Primary healthcare services ↑ 56: Nursing and ethical policy ↓ 
27: Patient healthcare cost and services ↓ 57: Consumer documentation ↓ 
28: Asthma ↓↓ 58: Childhood disparities ↑ 
29: Hospital charges and costs ↓ 59: Audit attendance category ↓ 
30: Causes and effects of anxiety and stress ↑↑↑ 60: Inappropriate audit attendance ↓ 

Our contextual topic modeling approach generated 60 topics. Among these topics, 15 showed significant increasing trends. These topics mainly deal with smart medical monitoring systems (Topic-42), different health pandemics and the importance of vaccination to control them (Topic-22), causes and effects of stress and anxiety (Topic-30), and health care services generally (Topic-08). Hot topics also include healthcare cost estimations (Topic-09), patient treatment risks (Topic-13), research on designing healthcare policy (Topic-47), 95 ci hospital risks (Topic-12), patient care quality (Topic-44), global financing for public health (Topic-34), 95 ci testing (Topic-02), surgery complications (Topic-38), health care research reviews (Topic-23),95 ci for patient disparities and transplantation (Topic-10 and Topic-01). Twelve topics showed significant decreasing trends. These topics covering care quality and management (Topic-45), Asthma and costs (Topic-28 &Topic-31), Hygiene compliance (Topic-16), Telemedicine technology (Topic-54), glucose control intervention (Topic-17), Antimicrobial compliance (Topic-11), Home intervention for patient care (Topic-14), Cost gained strategy (Topic-04), Physical and emotional effects on quality of life (QOL) (Topic-55), the role of nursing as a healthcare professional (Topic-39) and Resistance in MRSA isolation and transmission (Topic-07).

Beyond these topics, contextual topic modeling also discovered thirty-three more topics which were not shown significant increasing or decreasing trends but revealed various important subjects of the healthcare landscape. For example, infection-associated cost (Topic-24), patient treatment cost analysis (Topic-03), hospital charges and cost (Topic-29), Patient healthcare cost and services (Topic-27), children hospitalization rates (Topic-19), childhood disparities (Topic-58), Healthcare discrimination (Topic-53), Sexual substance and discrimination (Topic-49), HPV vaccination and immunization (Topic-21), investigation for coronavirus (Topic-43), Hypertension diabetes and medication (Topic-33), diabetes intervention (Topic-05), drug management and treatment (Topic-32), needs of cancer patients (Topic-46), pneumonia patients (Topic-25), nursing and ethical policy (Topic-56), nurses’ job satisfaction (Topic-36), patient care quality (Topic-44&Topic-48), primary healthcare services (Topic-26), health expenditure (Topic-06), consumer documentation (Topic-57), management of organizational processes (Topic-40), Healthcare quality indicators (Topic-50) inappropriate audit attendance and audit category (Topic-60 &Topic-59). Regarding education and technology, CTM discovered various topics like healthcare education (Topic-20), technology adoption and its acceptance (Topic-35), and Healthcare IoT and devices (Topic-51 & Topic-37). Beyond these topics, some deal deals with women like women's stigma (Topic-52), Brest cancer (Topic-15), and maternal inequalities (Topic-18).

For a profound understanding of the discovered topics, we also elaborated on the top-cited articles of the corpus in section (2.1) that can cross-validate the themes of the mostly contextual topics. Topic 42, Topic 51, Topic 37, Topic 54, and Topic 35, for instance, can be supported in light of “Paragraph 2.” (par.2). Both sections disused the applications of technology for healthcare. Another example would be the topics (Topic-24, Topic-04, Topic-38, Topic-29, Topic-27, Topic-31, Topic-03, Topic-9) are positioned with (par.3). These units mainly focused on cost and other relevant subjects like surgery and infections. Similarly, we can also cross-validate the rest of the topics. Another perspective of this study is that since we already encoded the context of topics in the modeling, the discovered topics are more coherent and self-explanatory (See Table 2).

4.3 Classification of Topic Trends

Based on the p-value, different groups are created for the trends of the contextualized topics. These classes were described in terms of arrow symbols. For example, the arrow symbol ↑(↓) showed an increasing (decreasing) trend, but not significant (if p > 0.05), and other arrow symbols like ↑↑(↓↓), ↑↑↑ (↓↓↓), ↑↑↑↑ (↓↓↓↓) showed a significantly increasing (decreasing) trend (if p < 0.05, p < 0.01, and p < 0.001, respectively as shown in (See Table 1). The annual distributions of these topics are depicted from the graphs (See Figure 3 and 4). The fifteen topics showed significantly increasing trends, twelve topics showed significantly decreasing trends thirty-three topics do not show significantly increasing or decreasing trends.

Figure 2.

Inter-topic distance and topic clusters.

Figure 2.

Inter-topic distance and topic clusters.

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Figure 3.

Graphical representations of topics (1-30) trends from 2000 to 2022.

Figure 3.

Graphical representations of topics (1-30) trends from 2000 to 2022.

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Figure 4.

Graphical representations of topics (31-60) trends from 2000 to 2022.

Figure 4.

Graphical representations of topics (31-60) trends from 2000 to 2022.

Close modal

4.4 Inter-Topic Distances Topic-Clusters and Correlations

We analyzed the inter-topic distance map of the most fitted contextualized topic model with (K=60). Figure 2 gives us three important clusters of the topics. Cluster-1 shows thirteen topics, and cluster-2 is dense and consisted of twenty-three topics. Cluster-3 consists of ten topics. We analyzed the different topics residing in these clusters which give important patterns, and correlations among the topics and various inter-topic research directions as follows:

The most significant topics covered under cluster-1 mainly focused on statistical analysis (95-CI) for different aspects of medical care, risk factors, associated cost, and re-hospitalization in severe medical conditions. Cluster-2 is dense and is further composed of various sub-groups. In the first group generalized topics (23, 32, 36, 46, 50) narrate the reviews on different areas of healthcare like job satisfaction, drug management, the need for cancer patients, and healthcare quality indicators.

The topics in the second group (48,49,53) correlate due to discrimination factors in healthcare generally and sexual substance discrimination in particular. The group-3 topics (4,11,16,21,22,55) point out the pandemic (covid-19), its effects, symptoms, antimicrobial compliance, the importance of hand hygiene, estimation of the cost of vaccination, and the immunity of the people against various infections. The group-4 (topics 6,14,15,18,41) is covering health expenditure and home interventions for many health impairments like breast cancer, migraine, and maternity in different economic situations. It also clarifies the side effects of overwork, especially in women. Group 5 (topics 3,9,17,30) focuses on cost estimation and patient treatment in different diseases, especially in glucose control, drugs, and insulin. Additionally, it highlights the effects of inappropriate audit attendance cost estimation.

Cluster-3 (topics 20,26,34,35,39,40,42,45,47,52) elaborates on the significance of healthcare education, nursing, geographic organizational management of finance in the healthcare sector, uses, and application of smart medical appliances in healthcare units. It points out the discomforts of women's stigma ethically. This cluster also shows its findings on research in healthcare policies, care, quality, and clinical management, provision of primary healthcare services, and technology adoption in the treatment process.

In this study, we developed a context-based topic modeling approach that uses a transformer-based deep learning model to enhance the semantic understanding of the topics. We developed a novel Healthcare Bert to provide word embeddings as context vectors. These embeddings are combined with a bag of words to generate contextual topics of the corpus in context-based topic modeling. The newly generated word embeddings also improve the topic's coherence score. In order to categorize the themes into hot and cold categories, we further explored the document term matrix (DTM), which CTM generates using statical analysis techniques. This study also sheds light on the correlation between the topics by plotting them on a two-dimensional plane with a visualization tool. In this way, various interesting topic patterns and inter-topic research directions are pointed out. By generating rich sentence embedding vectors of the corpus under study using transformers-based models, corpus tuning, mean pooling, and the hugging face tool, this research broadens the existing topic modeling approaches which do not include contextual information in the topic extraction process. Moreover, it improves the previous topic modeling methodologies that analyze the healthcare literature from a specific disciplinary viewpoint. This process has several restrictions and things to think about. We can look at online databases like Web of Science and PubMed when choosing a data source, however, the current study solely takes into account papers that are indexed in Scopus. The current study adds context information to the topics and further gives clustering and topic correlations analysis, in future studies we may add hierarchical semantic modeling and temporal perspective in this direction.

Writing—original draft, Inaam ul haq, M., writing—review and editing, Li, Q.M., and Inaam ul haq, M., All authors have read and agreed to the published version of the manuscript.

The data underlying this article is available in [Google Drive], at https://drive.google.com/drive/folders/1TOmO7VQYnhq-__CIU6Dqk5weFqkYAbH2?usp=sharing

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APPENDIX A

P-valueTopicTop Terms
0.0065 01:95 ci for transplantation transplantation, transplant,95, ci, crc, hr, hazard, blacks, stage, women, cancer, hispanics, survival, years, age, whites, hispanic, ses, among, disparities 
0.0004 02:95 ci testing hiv, aor, tb, ci, 95, testing, tuberculosis, risk, hcv, hcws, among, infected, positive, tested, infection, art, hepatitis, test, exposure, occupational 
0.355 03: Patient treatment cost analysis cost, costs, incremental, per, qaly, utility, placebo, treatment, patient, perspective, compared, gained, effectiveness, model, total, analysis, patients, trial, year, life 
0.0283 04: cost gained strategy qaly, cost, gained, strategy, incremental, model, effective, effectiveness, per, sensitivity, lifetime, perspective, life, years, screening, costs, year, treatment, savings, analysis 
0.653 05: diabetes intervention care, diabetes, primary, intervention, based, quality, screening, control, outcomes, practice, usual, management, self, intervention, controlled, trial, participants, group, effectiveness, practices 
0.978 06: Health expenditure households, health, china, expenditures, pocket, services, expenditure, household, payments, rural, utilization, inequality, urban, private, outpatient, children, insurance, areas, income, spending 
0.0004 07: Resistance in mrsa isolation and transmission isolates, outbreak, mrsa, aureus, resistant, isolation, ca, isolated, transmission, strains, resistance, hcw, environmental, hcws, infection, staphylococcus, antimicrobial, spread, tb, contact 
0.0283 08: Healthcare services hiv, services, health, facilities, healthcare, study, facility, rural, access, care, community, maternal, people, workers, district, women, service, delivery, tb, primary 
0.0018 09: Costs estimation billion, costs, million, indirect, disease, estimates, direct, burden, attributable, cost, fractures, per, estimated, incidence, year, estimate, total, annual, expectancy, countries 
0.0047 10:95 ci for patient disparities 95, ci, patients, hispanic, black, race, racial, white, whites, disparities, use, odds, ed, ethnicity, likely, opioid, among, blacks, receive, associated 
0.0033 11: antimicrobial compliance antimicrobial, compliance, hygiene, catheter, icus, hand, antibiotic, infection, surveillance, control, hospitals, units, bed, waste, rates, icu, feedback, infections, intensive, resistant 
0.0009 12:95 ci hospital risks 95, ci, risk, associated, hospital, rate, study, adverse, days, icu, events, rr, meta, mortality, studies, catheter, intervention, readmission, outcomes, ratio 
0.0001 13: Patient treatment risks patients, risk, hr, hazard,95, claims, treatment, ci, years, failure, matched, therapy, vs, disease, costs, date, diagnosis, higher, survival, compared 
0.0001 14: home intervention for patient care care, patients, home, intervention, group, usual, hf, clinic, primary, palliative, control, life, hospital, gp, randomized, months, program, groups, patient, telephone 
0.1611 15: Brest cancer cancer, breast, prostate, stage, lung, colorectal, survivors, screening, survival, crc, women, cervical, treatment, diagnosis, chemotherapy, african, black, diagnosis, racial, disparities 
0.0013 16: hygiene compliance Hand, waste, hygiene, doctors, compliance, physicians, staff, errors, workers, safety, reported, attitude, reporting, knowledge, nurses, training, pharmacists, healthcare, respondents, medical 
0.0162 17: glucose control intervention exercise, group, glucose, control, pressure, intervention, weeks, blood, training, week, participants, placebo, randomised, self, activity, controlled, randomized, difference, trial, groups 
0.7931 18: Maternal inequalities maternal, inequalities, health, mortality, birth, neonatal, inequality, factors, outcomes, countries, risks, socioeconomic, antenatal, income, deaths, low, pregnancy, 95, ci, women 
0.2904 19: children hospitalization rates rates, children, hospitalizations, trends, age, rate, hispanic, injury, uninsured, nationwide, hospitals, states, pediatric, blacks, per, medicaid 
0.6918 20: Healthcare education students, learning, training, practice, skills, education, implementation, evidence, knowledge, educational, change, teaching, development, course, clinical, interventions, research, behavior, consensus, nursing 
0.0504 21: hpv vaccination and immunization hpv, vaccination, immunization, vaccine, influenza, cervical, uptake, vaccines, recommendation, screening, women, parents, pregnant, coverage, adolescent, knowledge, among, crc, cancer, years 
0.0151 22: health pandemic and vaccination pandemic, covid,19,2020, influenza, vaccination,2021, vaccine, vaccines, willingness, workers, sars, coronavirus, hcws, cov,2019, personal, stress, work, public 
0.0047 23: Reviews on health care research Reviews, meta, articles, studies, systematic, search, trials, medline, searched, review, inclusion, outcomes, included, evidence, quality, research, interventions, literature, bias, criteria 
0.615 24: Infection-associated cost catheter, cdi, per, infection, associated, days, infections, cost, rates, surveillance, incidence, urinary, nosocomial, icus, 1000, hospital, rate, costs,000, cases 
0.9368 25: pneumonia patients acquired, ca, pneumonia, cdi, onset, patients, aureus, sepsis, children, hospital, associated, icu, infection, mrsa, resistant, infections, community, admission, respiratory, isolates 
0.383 26: Primary healthcare services care, palliative, health, services, primary, dementia, people, community, needs, mental, service, integrated, home, chronic, access, system, support, management, families, disease 
0.4127 27: patient healthcare cost and services care, costs, patients, healthcare, services, cost, medical, study, data, health, total, patient, use, service, claims, hospital, system, related, outpatient, year 
0.0283 28: Asthma ed, asthma, veterans, va, use, care, ptsd, pain, visits, mental, children, primary, reported, visit, emergency, medical, department, disorders, medication, disorder 
0.328 29: Hospital charges and costs Hospital, charges, costs, los, hospitalizations, stay, length, readmission, admissions, inpatient, readmissions, total, hospitalization, sepsis, cost, nationwide, associated, mortality, ed, admission 
0.0015 30: Causes and effects of anxiety and stress anxiety, stress, violence, depressive, depression, ptsd, sleep, burnout, psychological, work, symptoms, workers, covid, mental, physical, ci, associated, factors, risk, disorder 
0.0001 31: asthma and costs patients, vs, asthma, mean, copd, costs, group, symptoms, severe, months, days, 12, disease, pulmonary, per, total, year, one, median, treatment 
0.290 32: drug management and treatment treatment, clinical, drug, management, guidelines, drugs, guideline, therapy, recommendations, therapies, disease, adherence, patients, treatments, medication, consensus, evidence, pain, may, therapeutic, 
0.4756 33: hypertension diabetes and medication hypertension, diabetes, cam, drugs, prevalence, prescription, drug, use, years, age, women, population, medication, medications, adherence, prescribed, prescriptions, glucose, 95, among 
0.0162 34: Global financing for public health financing, countries, health, public, sector, government, policy, private, policies, china, equity, global, africa, expenditure, spending, country, european, world, funding, universal 
0.249 35: Technology adoption and its acceptance data, information, adoption, technology, ehr, healthcare, systems, model, use, privacy, system, acceptance, electronic, sharing, decision, records, research, user, users, used 
0.8325 36: Nurses job satisfaction job, burnout, satisfaction, dimensions, reliability, items, validity, nurses, safety, instrument, patient, culture, teamwork, nurse, work, nursing, leadership, factor, item, quality 
0.9195 37: Healthcare iot smart, scheme, algorithm, network, iot, networks, proposed, internet, remote, devices, energy, cloud, security, efficient, accuracy, wireless, secure, prediction, real, propose 
0.0004 38: Surgery Complications volume, surgeons, surgery, postoperative, undergoing, mortality, surgical, hospitals, readmission, patients, hospital, complications, outcomes, procedures, trauma, factors, risk, los, covid, day 
0.0039 39: role of nursing as healthcare professional nurses, nursing, nurse, professional, students, caring, role, practice, roles, ethical, working, blackwell, skills, ethics, professionals, team, gps, managers, members, themes 
0.0537 40: Management of organizational processes process, processes, organisational, methodology, organisations, waste, paper, approach, systems, organizational, simulation, theory, value, technology, chain, supply, decision, managers, complexity, conceptual 
0.5609 41: Side effect of overwork productivity, costs, migraine, hrqol, indirect, copd, direct, asthma, disease, impairment, pain, work, burden, chronic, life, healthcare, impact, reported, total, related 
0.0151 42: Smart medical monitoring system internet, mobile, remote, medical, smart, information, monitoring, privacy, devices, security, system, telemedicine, secure, communication, iot, cloud, proposed, technologies, technology, user 
0.2673 43: Coronavirus Investigation ct, viral, ml, sars, respiratory, cell, ant, syndrome, dose, cov, skin, infectious, virus, coronavirus, rapid, epidemic, detected, samples, min, water 
0.024 44: patient care quality and healthcare patient, care, quality, study, healthcare, research, satisfaction, practice, primary, nurses, health, nursing, process, using, information, professionals, service, design, support, communication 
0.0005 45: Care quality and management care, quality, management, clinical, organizations, system, standards, evidence', guidelines, systems, recommendations, development, new, medicine, based, stroke, practice, improvement, programs, delivery 
0.107 46: Needs of cancer patients Patients, cancer, caregivers, needs, palliative, patient, family, information, preferences, life, professionals, care, making, end, communication, families, decision, unmet, support, diagnosis 
0.0001 47: Research on health policy research, health, social, policy, community, implementation, services, equity, communities, methods, framework, barriers, approach, cultural, qualitative, people, evidence, interventions, factors, determinants 
0.0723 48: Patient care quality Care, quality, patient, hospital, hospitals, performance, Accreditation, satisfaction, beds, critical, measures, emergency, safety, nursing, physicians, centered, improvement, medical, physician, nurse 
0.634 49: sexual substance and discrimination sexual, substance, cam, discrimination, mental, health, use, veterans, women, services, abuse, seeking, stigma, among, alcohol, barriers, unmet, reported, disorders, men 
0.1388 50: health care quality indicators data, ehr, performance, quality, administrative, indicators, electronic, information, hospitals, records, clinical, used, systems, use, patient, using, measures, research, model, predictive 
0.4145 51: Healthcare devices skin, device, pressure, mechanical, detection, energy, monitoring, sensor, fall, flexible, algorithm, conventional, detect, power, technique, ms, imaging, accuracy, wearable, human 
0.4435 52: Women stigma stigma, women, hiv, experiences, sexual, social, partners, pregnant, themes, support, qualitative, pregnancy, participants, seeking, aids, young, men, depth, beliefs, interviews 
0.123 53: Health care discrimination care, health, discrimination, disparities, ethnic, access, racial, minority, dental, americans, preventive, cultural, quality, survey, children, insurance, provider, satisfaction, whites, providers 
0.0001 54: telemedicine technology telemedicine, technologies, digital, market, technology, internet, adoption, security, requirements, industry, consumers privacy, infrastructure, business, sector, integration, users, solutions, networks, consumer 
0.0011 55: Physical and emotional effects on quality of life (QOL) qol, pain, functioning, back, exercise, self, physical, life, cognitive, hrqol, symptom, parents, depression, symptoms, functional, intensity, scale, emotional, activity, distress 
0.793 56: Nursing and ethical policy nursing, article, ethical, policy, competence, practice, ethics, workforce, cultural, education, leadership, professional, concept, leaders, nurse, political, ways, must, changing, programs 
0.559 57: Consumer documentation consumer, legal, documentation, pharmaceutical, consumers, act, food, advance, youth, crisis, inappropriate, assistance, accreditation, continuity, market, success, travel, organ, encounters, reimbursement 
0.0813 58: Childhood disparities americans, childhood, disparities, minority, oral, ethnic, cardiovascular, african, prevention, morbidity, disease, racial, kidney, minorities, american, disorders, populations, include, heart, obesity 
0.1039 59: audit attendance category audit, attendance, category, cessation, episode, continuity, encounters, consultations, employment, netherlands, completion net, class, ms, falls, inappropriate, intensity, nutrition, knee, preventable, 
0.218 60: Inappropriate audit attendance audit, attendance, inappropriate, cessation, continuity, list, category, waiting, preventable, urgent, class, employment, completion, consultations, nutrition, ms, accreditation, spent, malaria, encounters 
P-valueTopicTop Terms
0.0065 01:95 ci for transplantation transplantation, transplant,95, ci, crc, hr, hazard, blacks, stage, women, cancer, hispanics, survival, years, age, whites, hispanic, ses, among, disparities 
0.0004 02:95 ci testing hiv, aor, tb, ci, 95, testing, tuberculosis, risk, hcv, hcws, among, infected, positive, tested, infection, art, hepatitis, test, exposure, occupational 
0.355 03: Patient treatment cost analysis cost, costs, incremental, per, qaly, utility, placebo, treatment, patient, perspective, compared, gained, effectiveness, model, total, analysis, patients, trial, year, life 
0.0283 04: cost gained strategy qaly, cost, gained, strategy, incremental, model, effective, effectiveness, per, sensitivity, lifetime, perspective, life, years, screening, costs, year, treatment, savings, analysis 
0.653 05: diabetes intervention care, diabetes, primary, intervention, based, quality, screening, control, outcomes, practice, usual, management, self, intervention, controlled, trial, participants, group, effectiveness, practices 
0.978 06: Health expenditure households, health, china, expenditures, pocket, services, expenditure, household, payments, rural, utilization, inequality, urban, private, outpatient, children, insurance, areas, income, spending 
0.0004 07: Resistance in mrsa isolation and transmission isolates, outbreak, mrsa, aureus, resistant, isolation, ca, isolated, transmission, strains, resistance, hcw, environmental, hcws, infection, staphylococcus, antimicrobial, spread, tb, contact 
0.0283 08: Healthcare services hiv, services, health, facilities, healthcare, study, facility, rural, access, care, community, maternal, people, workers, district, women, service, delivery, tb, primary 
0.0018 09: Costs estimation billion, costs, million, indirect, disease, estimates, direct, burden, attributable, cost, fractures, per, estimated, incidence, year, estimate, total, annual, expectancy, countries 
0.0047 10:95 ci for patient disparities 95, ci, patients, hispanic, black, race, racial, white, whites, disparities, use, odds, ed, ethnicity, likely, opioid, among, blacks, receive, associated 
0.0033 11: antimicrobial compliance antimicrobial, compliance, hygiene, catheter, icus, hand, antibiotic, infection, surveillance, control, hospitals, units, bed, waste, rates, icu, feedback, infections, intensive, resistant 
0.0009 12:95 ci hospital risks 95, ci, risk, associated, hospital, rate, study, adverse, days, icu, events, rr, meta, mortality, studies, catheter, intervention, readmission, outcomes, ratio 
0.0001 13: Patient treatment risks patients, risk, hr, hazard,95, claims, treatment, ci, years, failure, matched, therapy, vs, disease, costs, date, diagnosis, higher, survival, compared 
0.0001 14: home intervention for patient care care, patients, home, intervention, group, usual, hf, clinic, primary, palliative, control, life, hospital, gp, randomized, months, program, groups, patient, telephone 
0.1611 15: Brest cancer cancer, breast, prostate, stage, lung, colorectal, survivors, screening, survival, crc, women, cervical, treatment, diagnosis, chemotherapy, african, black, diagnosis, racial, disparities 
0.0013 16: hygiene compliance Hand, waste, hygiene, doctors, compliance, physicians, staff, errors, workers, safety, reported, attitude, reporting, knowledge, nurses, training, pharmacists, healthcare, respondents, medical 
0.0162 17: glucose control intervention exercise, group, glucose, control, pressure, intervention, weeks, blood, training, week, participants, placebo, randomised, self, activity, controlled, randomized, difference, trial, groups 
0.7931 18: Maternal inequalities maternal, inequalities, health, mortality, birth, neonatal, inequality, factors, outcomes, countries, risks, socioeconomic, antenatal, income, deaths, low, pregnancy, 95, ci, women 
0.2904 19: children hospitalization rates rates, children, hospitalizations, trends, age, rate, hispanic, injury, uninsured, nationwide, hospitals, states, pediatric, blacks, per, medicaid 
0.6918 20: Healthcare education students, learning, training, practice, skills, education, implementation, evidence, knowledge, educational, change, teaching, development, course, clinical, interventions, research, behavior, consensus, nursing 
0.0504 21: hpv vaccination and immunization hpv, vaccination, immunization, vaccine, influenza, cervical, uptake, vaccines, recommendation, screening, women, parents, pregnant, coverage, adolescent, knowledge, among, crc, cancer, years 
0.0151 22: health pandemic and vaccination pandemic, covid,19,2020, influenza, vaccination,2021, vaccine, vaccines, willingness, workers, sars, coronavirus, hcws, cov,2019, personal, stress, work, public 
0.0047 23: Reviews on health care research Reviews, meta, articles, studies, systematic, search, trials, medline, searched, review, inclusion, outcomes, included, evidence, quality, research, interventions, literature, bias, criteria 
0.615 24: Infection-associated cost catheter, cdi, per, infection, associated, days, infections, cost, rates, surveillance, incidence, urinary, nosocomial, icus, 1000, hospital, rate, costs,000, cases 
0.9368 25: pneumonia patients acquired, ca, pneumonia, cdi, onset, patients, aureus, sepsis, children, hospital, associated, icu, infection, mrsa, resistant, infections, community, admission, respiratory, isolates 
0.383 26: Primary healthcare services care, palliative, health, services, primary, dementia, people, community, needs, mental, service, integrated, home, chronic, access, system, support, management, families, disease 
0.4127 27: patient healthcare cost and services care, costs, patients, healthcare, services, cost, medical, study, data, health, total, patient, use, service, claims, hospital, system, related, outpatient, year 
0.0283 28: Asthma ed, asthma, veterans, va, use, care, ptsd, pain, visits, mental, children, primary, reported, visit, emergency, medical, department, disorders, medication, disorder 
0.328 29: Hospital charges and costs Hospital, charges, costs, los, hospitalizations, stay, length, readmission, admissions, inpatient, readmissions, total, hospitalization, sepsis, cost, nationwide, associated, mortality, ed, admission 
0.0015 30: Causes and effects of anxiety and stress anxiety, stress, violence, depressive, depression, ptsd, sleep, burnout, psychological, work, symptoms, workers, covid, mental, physical, ci, associated, factors, risk, disorder 
0.0001 31: asthma and costs patients, vs, asthma, mean, copd, costs, group, symptoms, severe, months, days, 12, disease, pulmonary, per, total, year, one, median, treatment 
0.290 32: drug management and treatment treatment, clinical, drug, management, guidelines, drugs, guideline, therapy, recommendations, therapies, disease, adherence, patients, treatments, medication, consensus, evidence, pain, may, therapeutic, 
0.4756 33: hypertension diabetes and medication hypertension, diabetes, cam, drugs, prevalence, prescription, drug, use, years, age, women, population, medication, medications, adherence, prescribed, prescriptions, glucose, 95, among 
0.0162 34: Global financing for public health financing, countries, health, public, sector, government, policy, private, policies, china, equity, global, africa, expenditure, spending, country, european, world, funding, universal 
0.249 35: Technology adoption and its acceptance data, information, adoption, technology, ehr, healthcare, systems, model, use, privacy, system, acceptance, electronic, sharing, decision, records, research, user, users, used 
0.8325 36: Nurses job satisfaction job, burnout, satisfaction, dimensions, reliability, items, validity, nurses, safety, instrument, patient, culture, teamwork, nurse, work, nursing, leadership, factor, item, quality 
0.9195 37: Healthcare iot smart, scheme, algorithm, network, iot, networks, proposed, internet, remote, devices, energy, cloud, security, efficient, accuracy, wireless, secure, prediction, real, propose 
0.0004 38: Surgery Complications volume, surgeons, surgery, postoperative, undergoing, mortality, surgical, hospitals, readmission, patients, hospital, complications, outcomes, procedures, trauma, factors, risk, los, covid, day 
0.0039 39: role of nursing as healthcare professional nurses, nursing, nurse, professional, students, caring, role, practice, roles, ethical, working, blackwell, skills, ethics, professionals, team, gps, managers, members, themes 
0.0537 40: Management of organizational processes process, processes, organisational, methodology, organisations, waste, paper, approach, systems, organizational, simulation, theory, value, technology, chain, supply, decision, managers, complexity, conceptual 
0.5609 41: Side effect of overwork productivity, costs, migraine, hrqol, indirect, copd, direct, asthma, disease, impairment, pain, work, burden, chronic, life, healthcare, impact, reported, total, related 
0.0151 42: Smart medical monitoring system internet, mobile, remote, medical, smart, information, monitoring, privacy, devices, security, system, telemedicine, secure, communication, iot, cloud, proposed, technologies, technology, user 
0.2673 43: Coronavirus Investigation ct, viral, ml, sars, respiratory, cell, ant, syndrome, dose, cov, skin, infectious, virus, coronavirus, rapid, epidemic, detected, samples, min, water 
0.024 44: patient care quality and healthcare patient, care, quality, study, healthcare, research, satisfaction, practice, primary, nurses, health, nursing, process, using, information, professionals, service, design, support, communication 
0.0005 45: Care quality and management care, quality, management, clinical, organizations, system, standards, evidence', guidelines, systems, recommendations, development, new, medicine, based, stroke, practice, improvement, programs, delivery 
0.107 46: Needs of cancer patients Patients, cancer, caregivers, needs, palliative, patient, family, information, preferences, life, professionals, care, making, end, communication, families, decision, unmet, support, diagnosis 
0.0001 47: Research on health policy research, health, social, policy, community, implementation, services, equity, communities, methods, framework, barriers, approach, cultural, qualitative, people, evidence, interventions, factors, determinants 
0.0723 48: Patient care quality Care, quality, patient, hospital, hospitals, performance, Accreditation, satisfaction, beds, critical, measures, emergency, safety, nursing, physicians, centered, improvement, medical, physician, nurse 
0.634 49: sexual substance and discrimination sexual, substance, cam, discrimination, mental, health, use, veterans, women, services, abuse, seeking, stigma, among, alcohol, barriers, unmet, reported, disorders, men 
0.1388 50: health care quality indicators data, ehr, performance, quality, administrative, indicators, electronic, information, hospitals, records, clinical, used, systems, use, patient, using, measures, research, model, predictive 
0.4145 51: Healthcare devices skin, device, pressure, mechanical, detection, energy, monitoring, sensor, fall, flexible, algorithm, conventional, detect, power, technique, ms, imaging, accuracy, wearable, human 
0.4435 52: Women stigma stigma, women, hiv, experiences, sexual, social, partners, pregnant, themes, support, qualitative, pregnancy, participants, seeking, aids, young, men, depth, beliefs, interviews 
0.123 53: Health care discrimination care, health, discrimination, disparities, ethnic, access, racial, minority, dental, americans, preventive, cultural, quality, survey, children, insurance, provider, satisfaction, whites, providers 
0.0001 54: telemedicine technology telemedicine, technologies, digital, market, technology, internet, adoption, security, requirements, industry, consumers privacy, infrastructure, business, sector, integration, users, solutions, networks, consumer 
0.0011 55: Physical and emotional effects on quality of life (QOL) qol, pain, functioning, back, exercise, self, physical, life, cognitive, hrqol, symptom, parents, depression, symptoms, functional, intensity, scale, emotional, activity, distress 
0.793 56: Nursing and ethical policy nursing, article, ethical, policy, competence, practice, ethics, workforce, cultural, education, leadership, professional, concept, leaders, nurse, political, ways, must, changing, programs 
0.559 57: Consumer documentation consumer, legal, documentation, pharmaceutical, consumers, act, food, advance, youth, crisis, inappropriate, assistance, accreditation, continuity, market, success, travel, organ, encounters, reimbursement 
0.0813 58: Childhood disparities americans, childhood, disparities, minority, oral, ethnic, cardiovascular, african, prevention, morbidity, disease, racial, kidney, minorities, american, disorders, populations, include, heart, obesity 
0.1039 59: audit attendance category audit, attendance, category, cessation, episode, continuity, encounters, consultations, employment, netherlands, completion net, class, ms, falls, inappropriate, intensity, nutrition, knee, preventable, 
0.218 60: Inappropriate audit attendance audit, attendance, inappropriate, cessation, continuity, list, category, waiting, preventable, urgent, class, employment, completion, consultations, nutrition, ms, accreditation, spent, malaria, encounters 
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