Obstacles and Features of Health Information Systems: A Systematic Literature Review

Introduction

In this day and age, the healthcare industry is increasingly reliant on technology. Almost all registrations, including health care, are following suit. In the near future, we may use blockchain to trade health data. Since then, IT solutions have been used by various healthcare industries. There are GP/pharmacy/lab information systems as well as GP information systems. All of them are HIS (HISs). The usage of HIS may improve the quality and efficiency of patient care. Delivering high-quality treatment and securing compensation is dependent on a well-operating Health Information System (HIS). The gathering of trustworthy data on population health, system performance, and population health is promoted by an effective health information system.

Background

Today, a growing number of health information management are being utilized by a range of health care organizations to help their operations. Health information systems research work is sparse, and no comprehensive review of the present status of HISs has yet been described. HIS data is difficult to understand and classify in continuation over this. This article examines and Health information systems from a variety of perspectives. Desktop computers used to do the task, but nowadays mobile phones and central computer servers are increasingly common. Each HIS has its own set of characteristics due to the wide variety of healthcare settings and HIS implementation strategies. Health Information Systems Health information systems used to just track medical history, but now they do much more. All of HIS's qualities are critical to its performance. In addition to clinical notes and medication management, HIS also manages electronic health records (EHR). Creating, utilizing, and maintaining these HISs is a challenge. Many studies have shown problems that are similar to those seen in HISs. HISs have been studied extensively, but it's not clear to whom and in what areas they may aid. The goal of this research is to use an SLR to evaluate HISs. For the time being, we'll focus on mapping stakeholders, domains, deployments, features, and issues (obstacles). All of these issues have come up in previous assessments. In order to locate remedies, these evaluators looked at the papers. However, no current research study has been able to identify and quantify all of the challenges. Stakeholder concerns may be better understood via a literature study. Both academics and practitioners might benefit from the findings. It's possible for practitioners to recognize issues and incorporate them into the system. This work may be improved by researchers focusing on the issues. Kitchenham et al. used the same approach for software engineering as well. As a result, we focused on a subset of HISs rather than the whole system. . (Kitchenham, 2019)

Current Hypothesis

According to Kitchenham, data from 136 original studies was utilized to assess the current state of HISs. Hospital information systems (HIS) and related subjects such as diagnostics and surgery were covered in a number of papers. Health information systems (HISs) are anticipated to have an effect on the extent of stakeholder categories and qualities reported. Usage issues appears to be the only healthcare sector-specific subcategory among many of the obstacles.

Methodology:

All of the HISs in the world, as well as their features, stakeholders, and characteristics as well as barriers are listed in a systematic literature review.

1.1 Features

We used Kitchenham et al. [22]'s software engineering ideas from Cochrane Collaboration.

As seen in Fig. 1, We iterated till we found a good search string. Included and excluded studies were determined. Quality evaluation weeded out low-quality research. Fourth, we created a data extraction form (healthcare domains, stakeholders).

 

1.2 Stakeholders

We identified 41 unique stakeholders based on HIS usage (Table 6). The largest group of HIS users, physicians and nurses were among the stakeholders. These are the people who are affected yet do not utilize the system. A total of 45 articles referred to IT professionals. Since papers focused on system users, patients were seldom studied in healthcare. Study participants included both direct and indirect users and IT experts. (Charters, 2018)

Table 6 The number of publications in which each of the 136 key stakeholders is referenced.

Findings

The 136 studies included in the analytic set were synthesized to include all stakeholders, healthcare domains, features, and impediments. All ingredients were prepared as directed Features, Stakeholders,, Healthcare and Domains. After discovering duplication and misspelled words, bottom-up catchall phrase sorting was performed. The original inventor gave anything a label and a rank. Other scholars have classified a part of the story. This study's categorization came after a plenary discussion. There were 73 components and 69 concerns to health care.

Results

Thirteen hundred and forty full-text publications were found by the SLR. Our findings included 33 areas, 41 stakeholder groups, 73-character characteristics and 69 impediments. We explored how these aspects interacted and came up with solutions to the problems that had been highlighted. Quality, use, and maintenance were stressed in terms of technical and operational aspects. To go forward with HIS research, hurdles must be addressed from every angle.

Conclusions

Based on information from three sources, researchers analyzed 136 HIS papers during the previous decade. In-depth coverage of the healthcare industry's many stakeholders and issues This camera should be able to capture images of the human eye. The majority of HIS research focuses on inpatient therapy. Now include and technological stakeholders were identified. The general information system and the health care setting were addressed. A total of around 70 issues are associated with his dependence. There is a broad range of characteristics and obstacles. HIS researchers and users will benefit from this. Assist management in making decisions or implementing new processes. Scientists and software designers may use it to improve healthcare information systems (HIS). The findings of this research might pave the way for new directions in HIS study. An HIS includes changes will be built using SLR. (Budgen, 2017)

 

References

1.      Kitchenham, Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2019). Systematic literature reviews in software engineering – A systematic literature review. Information and Software Technology, 51(1), 7–15. https://doi.org/10.1016/j.infsof.2008.09.009

2.      Charters, Budgen, D., Turner, M., Kitchenham, B., Brereton, O. ., & Linkman, S. (2018). Objectivity in Research: Challenges from the Evidence-Based Paradigm. 2009 Australian Software Engineering Conference, 73–80. https://doi.org/10.1109/ASWEC.2009.25

3.      Budgen, Charters, S., Turner, M., Brereton, P., Kitchenham, B., & Linkman, S. (2017). Investigating the applicability of the evidence-based paradigm to software engineering. Proceedings of the 2006 International Workshop on Workshop on Interdisciplinary Software Engineering Research, 7–14. https://doi.org/10.1145/1137661.1137665

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Mobile phone data statistics as a dynamic proxy indicator in assessing regional economic activity and human commuting patterns

 Introduction:

Phones are being utilized for a variety of purposes beyond texting. There is no way to track a landline phone's owner's whereabouts. Mobile phone habits may provide demographic and economic information. As a result, regional and national outcomes may be affected. It is a reliable source of information on population, tourism, and transit. If you're commuting to work, you may now study it. The position of a phone is determined by the cell's cellular base station. Out-of-range phones connect to the base stations. A mobile phone is required to make and receive phone calls. There is enough information to be gleaned from the first few calls. Using many cellphone base stations to make phone calls reveals a person's habits. A person's location may be inferred if they begin chatting from one base station and wind up talking from another. Call and/or SMS data may also be accessed for investigation purposes. Trends in mobile phone use might provide demographic information. For both social and economic reasons, LMT data was analyzed. Mobile phone data analytics is also possible. This enabled researchers to keep tabs on their subjects. On the 25th of May, 2018, the GDPR went into force. (Spadon, 2019)

Background:

GDPR applies to EU individuals' personal information. It was become illegal to transfer individual phone numbers and do other related conduct under the GDPR. Each mobile base station's total calls and unique users were submitted to researchers. Track down cell towers. Every 15 minutes, on average, calls and SMS were received. During this time period, there were a total of unique users. The area that a base station may provide coverage. They'd been tracked down. In a data loss of 19 records, more affluent areas saw a decrease in economic growth. Local economic hotspots were highlighted in 2017–2018 by changes in transportation and internal activities. employing mobile phone data to keep tabs on business activities and travel habits. a. A prior municipal research on seasonality and mobile phone use was used to compare these results with the new ones. In 2015, 2016, 2017, and 2018, they were categorized. Finally, we assessed the efficiency and dynamism of economic activity. Regional economic growth trends in Latvia were discovered by people travelling to and from their jobs. Data and actions may be better understood using this tool. Time is required for visualization and analytics. Mobile applications are widely used and have several advantages. Our approach is a combination of empirical and computational visual analytics.

Methodology

Every time you make or receive a telephone conversation or text or email, any mobile phone network operator will send customers a CDR (Contact Detail Record) file. It is feasible to ascertain a person's precise location at the beginning of a phone conversation since the locations of all base stations are publicly available. The maximum number of phone calls and texts, the number of random users, and the date/time intervals are all included in each database record. Location and antennas type of cell site is included. The regional growth index may be used for real-time or regular assessment, according to the authors. Using this method, regional governments may conduct a strategic gap analysis to monitor the execution of their plans.

 

By frequently assessing the strategic direction attained by local jurisdictions over time, regional administrations may use additional central outcome measures. As a consequence, a new index was established based on fake data from Latvian mobile telephone consumers. (Arhipova, 2018)

 

 

 

Findings 

From 2015 through 2018, 119 factors (a linear combination of total mobile telephone operations and unique visitors for all municipality) were constructed for each workday to categories townships based on the economic development. Using a Factorial rotation, the PCA was performed for the years 2015 to 2016, 2017 to 2018, as well as 2018 to 2019. Questionnaire appropriateness testing (KMO) was used to examine the applied PCA. With KMO values around 0.8 and 1, it is clear that the sample size is enough. On workdays, the first functional unit (PC) has high values, whereas the second PC has high values on Fridays and Saturdays for towns with little business growth. The KMO is 0.990, which explains 67.6 percent of the overall variance. There is a 71.0 percent variation in the wide variety of experiences in 2017 and a 77.7 percent variation in 2018 based on PCA results that are relevant. Figure 2 shows the average values of the critical parts for during the week and weeks (Figure 3 as shown). (Šćepanović, 2019)

Principal component analysis information revealed that there have been eight sets of townships in Latvia (including working days and weekend hours), which were as follows: 2015–2016, 2018, and 2019. As a first step, the effectiveness of the commercial activity plan was assessed using equation, with the effectiveness curves ranging from 40 percent to 100 percent of their maximum value. The efficiency curve is a valuable tool for evaluating the characteristics of various municipality and other organizations. (Wen, 2018)

Conclusions

This might monitor the region's economic success. The time between working days and vacations varies greatly. Based on Latvian municipal statistics, these eight categories We utilise each town's effectiveness curve. Aspects of season and economy Latvia's economy improved in 2017. Disinterested everywhere. In 2018, the number of communities with 95% to 100% productivity doubled. Saturdays and holidays are slower. We need more vacation. Prioritisation messes up resources Summer in Latvia is hot. Revise a town's strategic framework. Monitor in real-time or on request. Keep an eye on the strategy for strategic flaws. Within a city or county strategic direction may be seen. A Latvian phone data is used. Using anonymized smartphones for Compliance. Routing data from commuters. Human commuters signify freedom. Intensity changes day to day imply population shifts In 2017 and 2018, it was 7am-5pm. Weekends and holidays are busier. 5–7 p.m. We developed a technique to analyse internal population movements and seasonality.

 

References

 

1.      Spadon, Carvalho, A. C. P. L. F. de, Rodrigues-Jr, J. F., & Alves, L. G. A. (2019). Reconstructing commuters network using machine learning and urban indicators. Scientific Reports, 9(1), 11801–11813. https://doi.org/10.1038/s41598-019-48295-x

2.      Arhipova, Berzins, G., Brekis, E., Binde, J., Opmanis, M., Erglis, A., & Ansonska, E. (2018). Mobile phone data statistics as a dynamic proxy indicator in assessing regional economic activity and human commuting patterns. Expert Systems, 37(5), n/a–n/a. https://doi.org/10.1111/exsy.12530

3.      Šćepanović, Mishkovski, I., Hui, P., Nurminen, J. K., & Ylä-Jääski, A. (2019). Mobile phone call data as a regional socio-economic proxy indicator. PloS One, 10(4), e0124160–e0124160. https://doi.org/10.1371/journal.pone.0124160

4.      Wen, Hsu, C.-S., & Hu, M.-C. (2018). Evaluating neighborhood structures for modeling intercity diffusion of large-scale dengue epidemics. International Journal of Health Geographics, 17(1), 9–9. https://doi.org/10.1186/s12942-018-0131-2