The Impact of Artificial Intelligence Integration on Minimizing Patient Wait Time in Hospitals

Reduced patient wait-times benefit not just patients' health but also the overall efficiency of the healthcare system, which is particularly crucial given the aging population and rising demand for medical services in recent decades. Reducing the time that outpatients have to wait is one of the most crucial actions that must be taken to improve the patient experience. Artificial intelligence and machine learning may be applied in health care and medicine to enhance insights, reduce waste and wait time, and increase speed, service efficiency, accuracy, and efficiency. The purpose of this research is to determine whether or not the deployment of AI in hospital management system help reduce the amount of time that patients have to wait for their appointments. The Random Forest Regression, Pairwise multiple regression, and the pairwise Pearson correlation have been performed. This research also included additional features such as the number of the office personnel, the number of doctors, the quantity of equipment, and the health expenses in order to eliminate any potential omitted variable biases. According to the findings of the Random Forest Regression, the integration of AI and ML seems to be required to cut down on the amount of time that patients have to wait. The size of the office personnel, the number of doctors, and the number of pieces of equipment are found to be significant factors in lowering the amount of time spent waiting. It was determined that the aspect of the cost was the least significant in terms of reducing the amount of time spent waiting. According to the findings of our study, the healthcare care center needs to expand the integration of AI in order to cut down on the waiting time for patients and to improve the overall experience they provide for them. The findings also suggest that wait times depend on many factors. Thus, focusing on a few factors does not significantly reduce wait time

A bibliometric analysis of quality in health care services

The health system is a social structure based on a set of people and actions designed to maintain and improve the health of the population. The wide access of patients and the diversity of segments that offer healthcare services, give us the opportunity to know, understand and analyze the main tools related to the quality of healthcare services, understanding their theories, techniques, characteristics and their relationships. This work aims to perform a literature review on the important aspects related to the quality of service in health, such as the types of health institutions, such as different ways of evaluating the quality of service, patient satisfaction and the impact of both on the practical service offered. From this study, it was possible to explore the quality issues in the health service: the improvement of the process, diagnosis and quality indicators, perception and assessment of the patient and the identification of the factors that influence quality. The application of some methodologies and the use of support tools were identified, systematizing the execution of data analysis and process mapping

Intelligent Patient Flow Management System at a Primary Healthcare Center : The Effect on Service Use and Costs

An intelligent patient flow management system (IPFM) was piloted at a large primary healthcare center in Finland in August 2017. The goals of the system are to help patients avoid unnecessary calls and visits to their health center and to enhance the use of professional resources through more streamlined patient pathways and the re-allocation of professionals from assessment tasks to actual patient care. These goals should be reflected in the decreased service costs through optimized contact forms. Using multiple regression analysis, we studied the associations between IPFM and patients' service utilization (17,943 patients; 73,038 service contacts) during the first five months of the pilot in 2017. The results indicated that the use of IPFM by the patient was associated with a decrease of EUR 31 in the total service costs of the patient in the study period. This decrease is 14% of patient's average total service cost.Peer reviewe

Value generation, potential, and restrictions on implementing Klinik Access in health care centers - Case study

The health care sector has been introduced by different new digital tools as well as applications of artificial intelligence (AI) to both enhance the system and enable new sources of value creation. The needs for these emerge from increasing expenditure on the health care domain as well as scarcity in resources to perform health interventions. One application used in several health care providers acts both as an interface for patients to seek for health care services as well as an intelligent tool for health care professionals to conduct health needs assessments with symptom-based AI predictions on what might explain the ailments of the patient. Previous studies propose that the use of this tool would have positive impact on cost effectiveness, as well as describe different value creation mechanisms of the tool. This study aims to describe how value is being perceived from the professionals’ point of view with a selected health care providers to gain understanding on how on the workers’ level the value both is realized and what might restrict value potential. The result of this thesis concludes five themes that acts as domains for value creation and its restrictions. The configuration of this have affected the value creation. The other value dimensions identified were Individuals’ work, System, Applicability, and Data. This thesis presents several specific value creation mechanisms as well as sup-ports previous studies. This study proposes that there could be value to be harnessed by changes in the tool configuration in the system as well as the processes in the health care organization. This study proposes need for clarification of the scientific domain of digital tools as part of health care organizations’ operations.Terveydenhuoltosektorilla ollaan viime aikoina otettu käyttöön uusia digitaalisia työkaluja sekä tekoälyratkaisuja parantaakseen järjestelmän toimintaa ja mahdollistaakseen uusia arvonluonnin lähteitä. Tarve näille kumpuaa kasvavasta terveydenhuollon kustannuksista ja terveydenhuollon resurssien niukkuudesta. Yksi useiden terveydenhuollon palveluntarjoajien käyttämä sovellus toimii sekä käyttöliittymänä terveyspalveluihin hakeutuville potilaille, että työkaluna hoidontarpeen arvion tekemiseen. Työkalu luo jälkimmäistä varten oireperusteisen ennusteen potilaan tilaa selittävistä diagnoosivaihtoehdoista. Edelliset tutkimukset ovat osoittaneet työkalulla olevan kustannustehokkuusvaikutuksia sekä muodostaneet käsitystä arvonluonnin mekanismeista. Tämä työ pyrkii muodostamaan kuvan työkalun arvonluonnista hoitohenkilökunnan näkökulmasta, tavoitteena ymmärtää vaikutuksia palveluntarjoajan kokemaan arvoon sekä tämän luonnin kipukohtiin. Tämä työ esittää, että arvo realisoituu ammattilaisten näkökulmasta viidellä eri ulottuvuudella, jotka ovat Konfiguraatio, Yksilötyö, Systeemi, Soveltuvuus, ja Data. Lisäksi tämä työ esittää yksityiskohtaisemmin arvonluonnin mekanismeja sekä haasteita. Tämän työn tulosten puolesta on oletettavaa, että muutoksilla työkalun konfiguraatioon tai organisaation toimintamalleihin voidaan saavuttaa vielä saavuttamattomia hyötyjä. Lisäksi tämä tutkimus esittää tarpeen lisätutkimukselle digitaalisten työkalujen arvonluonnille terveydenhuoltosektorilla

Digitaalisten terveysteknologioiden arvon muodostuminen – monitapaustutkimus älykkään potilasvirtajärjestelmän toteutuksesta

Aging populations set challenges to healthcare systems on a global scale, thus increasing demand for new technological solutions. As a result, investments in new digital health technologies are constantly increasing. Yet the value of these solutions is difficult to measure. Although many evaluation models for digital health interventions exist, there seems to be a lack of proper explanation of the mechanisms behind the value formulation. This thesis explores a novel approach to evaluate and structure value formulation for digital health interventions. Utilizing a CIMO-logic (context, intervention, mechanisms, and outcomes) in a multiple case study, this study set out to apply a recently developed value formulation model, PROVE-IT, for a digital health intervention for seeking of treatment and triage purposes. The research problem was divided into three objectives. The first aim was to discover the mechanisms explaining the functionality of the examined intervention. Second, the thesis explored how the relationship between the mechanisms and outcomes can be measured. Third, the ultimate goal was to develop the existing model to be more practical and generalizable for all digital health interventions. The CIMO-logic was perceived to be a suitable tool for evaluating digital health interventions and their dynamics. Despite differences in contexts, the mechanisms for each case were found to be very similar, thus questioning the generalizable characteristic of the model. As a result, this research suggests further actions to clarify each section in the PROVE-IT model besides presenting means to apply practical metrics to operationalize the model. This thesis contributes to the existing evaluation literature by providing a new approach for value formulation by emphasizing the mechanism perspective. Furthermore, this study provides insight into the practical use of value formulation model to be utilized in sales narratives of health technology companies.Ikääntyvien väkimäärän kasvu haastaa terveydenhuoltojärjestelmiä maailmanlaajuisesti kasvattaen kysyntää uusille terveysteknologisille ratkaisuille. Tästä johtuen uusien digitaalisten terveysteknologisten investoinnit ovat jatkuvassa kasvussa. Näiden ratkaisujen arvoa on kuitenkin haastavaa mitata. Vaikka monia arviointityökaluja on kehitetty kyseisille teknologioille, puutteita ilmenee erityisesti arvonluonnin mekanismien selittämisessä. Tässä työssä tutkittiin uudenlaista lähestymistä digitaalisten terveysteknologioiden arvon määrittämiseksi ja jäsentämiseksi. Hyödyntäen CIMO-logiikkaa (konteksti, interventio, mekanismi, ja vaikutus) monitapaustutkimuksessa työ pyrkii soveltamaan arvon muodostumisen PROVE-IT-mallia hoidon hakeutumiseen ja hoidontarpeen arviointiin suunnatulle digitaaliselle terveysteknologialle. Tutkimusongelma kiteytyy kolmeen tavoitteeseen. Ensimmäisenä tavoitteena on löytää mekanismit, jotka selittävät tarkastellun terveysteknologian toimivuutta. Toisena tavoitteena on tarkastella miten havaittuja mekanismeja ja niiden suhdetta vaikutuksiin voisi mitata. Kolmantena päätavoitteena on mallin kehittäminen käytännönläheisemmäksi ja yleistettävämmäksi. Diplomityössä havaittiin CIMO-logiikan olevan toimiva tapa jäsentää arvon muodostumista ja sen mekanismeja. Kontekstien erovaisuudesta huolimatta mekanismien havaittiin olevan hyvin samankaltaiset eri tapaustutkimusten välillä, mikä kyseenalaistaa mallin yleistävän luonteen. Tämän vuoksi, työ antaa pohjan jatkotoimenpiteille mallin selkeyttämiseksi sekä suuntaviivoja sen operationalisoinniksi. Tämä tutkimus täydentää arviointityökalujen kirjallisuutta tarjoamalla uuden näkökulman arvon muodostumiseen mekanismeja korostaen. Lisäksi työ tarjoaa näkemystä arvon muodostamisen osoittamisesta yritysten myynnin tukemiseksi