1
|
Kumar D, Suthar N. Predictive analytics and early intervention in healthcare social work: a scoping review. SOCIAL WORK IN HEALTH CARE 2024; 63:208-229. [PMID: 38349783 DOI: 10.1080/00981389.2024.2316700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 01/05/2024] [Indexed: 02/15/2024]
Abstract
This scoping review investigates the untapped potential of predictive analytics in healthcare social work, specifically targeting early intervention frameworks. Despite the escalating attention predictive analytics has garnered across multiple disciplines, its tailored application in social work remains notably sparse. This study endeavors to fill this lacuna by meticulously reviewing the extant literature and delineating the prospective advantages and inherent constraints of integrating predictive analytics into healthcare social work. The outcomes of this inquiry enrich the prevailing dialogue on the utility of predictive analytics in healthcare, offering indispensable perspectives for practitioners and policymakers in the social work domain.
Collapse
Affiliation(s)
- Dinesh Kumar
- Faculty of Business and Applied Arts, Lovely Professional University, Mittal School of Business, Phagwara, India
| | - Nidhi Suthar
- Administration, Pomento IT Services, Hisar, India
| |
Collapse
|
2
|
Barbieri C, Neri L, Stuard S, Mari F, Martín-Guerrero JD. From electronic health records to clinical management systems: how the digital transformation can support healthcare services. Clin Kidney J 2023; 16:1878-1884. [PMID: 37915897 PMCID: PMC10616428 DOI: 10.1093/ckj/sfad168] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Indexed: 11/03/2023] Open
Abstract
Healthcare systems worldwide are currently undergoing significant transformations in response to increasing costs, a shortage of healthcare professionals and the growing complexity of medical needs among the population. Value-based healthcare reimbursement systems are emerging as an attempt to incentivize patient-centricity and cost containment. From a technological perspective, the transition to digitalized services is intended to support these transformations. A Health Information System (HIS) is a technological solution designed to govern the data flow generated and consumed by healthcare professionals and administrative staff during the delivery of healthcare services. However, the exponential growth of digital capabilities and applied advanced analytics has expanded their traditional functionalities and brought the promise of automating administrative procedures and simple repetitive tasks, while enhancing the efficiency and outcomes of healthcare services by incorporating decision support tools for clinical management. The future of HIS is headed towards modular architectures that can facilitate implementation and adaptation to different environments and systems, as well as the integration of various tools, such as artificial intelligence (AI) models, in a seamless way. As an example, we present the experience and future developments of the European Clinical Database (EuCliD®). EuCliD is a multilingual HIS used by 20 000 nurses and physicians on a daily basis to manage 105 000 patients treated in 1100 clinics in 43 different countries. EuCliD encompasses patients' follow-up, automatic reporting and mobile applications while enabling efficient management of clinical processes. It is also designed to incorporate multiagent systems to automate repetitive tasks, AI modules and advanced dynamic dashboards.
Collapse
Affiliation(s)
- Carlo Barbieri
- Global Digital Transformation and Innovation, Clinical Digital Center of Excellence, Fresenius Medical Care, Crema Italy
| | - Luca Neri
- Global Medical Office, Clinical Advanced Analytics, Fresenius Medical Care, Crema Italy
| | - Stefano Stuard
- Global Medical Office, Clinical and Therapeutic Governance, Fresenius Medical Care, Naples, Italy
| | - Flavio Mari
- Global Digital Transformation and Innovation, Clinical Digital Center of Excellence, Fresenius Medical Care, Crema Italy
| | - José D Martín-Guerrero
- Intelligent Data Analysis Laboratory, Department of Electronic Engineering, ETSE -UV, Universitat de València, Valencia, Spain
| |
Collapse
|
3
|
Bhati D, Deogade MS, Kanyal D. Improving Patient Outcomes Through Effective Hospital Administration: A Comprehensive Review. Cureus 2023; 15:e47731. [PMID: 38021686 PMCID: PMC10676194 DOI: 10.7759/cureus.47731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 10/26/2023] [Indexed: 12/01/2023] Open
Abstract
This comprehensive review delves into the critical role of effective hospital administration in shaping patient outcomes within the healthcare ecosystem. Exploration of key components, strategies, measurement methodologies, and future trends elucidates the multifaceted nature of hospital administration. Key findings underscore the profound impact of administrative decisions and practices on patient safety, satisfaction, and overall well-being. The review highlights the importance of patient-centred care and interdisciplinary collaboration for enhancing patient outcomes. It emphasises the significance of data-driven measurement and benchmarking, which are instrumental in assessing hospital performance and fostering continuous improvement. Looking ahead, emerging technologies, evolving healthcare policies, and persistent challenges are drivers of change in healthcare administration. However, amidst these transformations, the overarching message remains consistent: effective hospital administration is integral to improving patient outcomes. The conclusion calls for a collective commitment from healthcare leaders and policymakers to prioritise the development of capable administrators, invest in technology, promote value-based care, and address healthcare disparities. This collaborative effort ensures that the pursuit of better patient outcomes remains at the forefront of healthcare administration, ultimately shaping the future of healthcare for generations to come.
Collapse
Affiliation(s)
- Deepak Bhati
- Hospital Administration, School of Allied Health Sciences, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| | - Meena S Deogade
- Ayurveda Pharmacology, All India Institute of Ayurveda, New Delhi, IND
| | - Deepika Kanyal
- Hospital Administration, School of Allied Health Sciences, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education & Research, Wardha, IND
| |
Collapse
|
4
|
Eysenbach G, Mull K, Santiago-Torres M, Miao Z, Perski O, Di C. Smoking Cessation Smartphone App Use Over Time: Predicting 12-Month Cessation Outcomes in a 2-Arm Randomized Trial. J Med Internet Res 2022; 24:e39208. [PMID: 35831180 PMCID: PMC9437788 DOI: 10.2196/39208] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/03/2022] [Accepted: 07/13/2022] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes. OBJECTIVE In the context of a randomized trial comparing 2 smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership. METHODS Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app. RESULTS For iCanQuit, participants were clustered into 3 groups: "1-week users" (610/1069, 57.06%), "4-week users" (303/1069, 28.34%), and "26-week users" (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio [OR] 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: "1-week users" (695/1064, 65.32%) and "3-week users" (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app. CONCLUSIONS Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful.
Collapse
Affiliation(s)
| | | | | | - Zhen Miao
- University of Washington, Seattle, US
| | | | | |
Collapse
|
5
|
Marwaha JS, Kvedar JC. Crossing the chasm from model performance to clinical impact: the need to improve implementation and evaluation of AI. NPJ Digit Med 2022; 5:25. [PMID: 35241790 PMCID: PMC8894388 DOI: 10.1038/s41746-022-00572-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 01/21/2022] [Indexed: 11/09/2022] Open
Affiliation(s)
- Jayson S Marwaha
- Beth Israel Deaconess Medical Center, Boston, MA, USA. .,Harvard Medical School, Boston, MA, USA.
| | - Joseph C Kvedar
- Harvard Medical School, Boston, MA, USA.,Mass General Brigham, Boston, MA, USA
| |
Collapse
|
6
|
Marwaha JS, Landman AB, Brat GA, Dunn T, Gordon WJ. Deploying digital health tools within large, complex health systems: key considerations for adoption and implementation. NPJ Digit Med 2022; 5:13. [PMID: 35087160 PMCID: PMC8795422 DOI: 10.1038/s41746-022-00557-1] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 12/22/2021] [Indexed: 11/09/2022] Open
Abstract
In recent years, the number of digital health tools with the potential to significantly improve delivery of healthcare services has grown tremendously. However, the use of these tools in large, complex health systems remains comparatively limited. The adoption and implementation of digital health tools at an enterprise level is a challenge; few strategies exist to help tools cross the chasm from clinical validation to integration within the workflows of a large health system. Many previously proposed frameworks for digital health implementation are difficult to operationalize in these dynamic organizations. In this piece, we put forth nine dimensions along which clinically validated digital health tools should be examined by health systems prior to adoption, and propose strategies for selecting digital health tools and planning for implementation in this setting. By evaluating prospective tools along these dimensions, health systems can evaluate which existing digital health solutions are worthy of adoption, ensure they have sufficient resources for deployment and long-term use, and devise a strategic plan for implementation.
Collapse
Affiliation(s)
- Jayson S Marwaha
- Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA. .,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Adam B Landman
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Gabriel A Brat
- Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - William J Gordon
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| |
Collapse
|
7
|
Nikolova-Simons M, Golas SB, den Buijs JO, Palacholla RS, Garberg G, Orenstein A, Kvedar J. A randomized trial examining the effect of predictive analytics and tailored interventions on the cost of care. NPJ Digit Med 2021; 4:92. [PMID: 34083743 PMCID: PMC8175712 DOI: 10.1038/s41746-021-00449-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 03/16/2021] [Indexed: 12/13/2022] Open
Abstract
This two-arm randomized controlled trial evaluated the impact of a Stepped-Care intervention (predictive analytics combined with tailored interventions) on the healthcare costs of older adults using a Personal Emergency Response System (PERS). A total of 370 patients aged 65 and over with healthcare costs in the middle segment of the cost pyramid for the fiscal year prior to their enrollment were enrolled for the study. During a 180-day intervention period, control group (CG) received standard care, while intervention group (IG) received the Stepped-Care intervention. The IG had 31% lower annualized inpatient cost per patient compared with the CG (3.7 K, $8.1 K vs. $11.8 K, p = 0.02). Both groups had similar annualized outpatient costs per patient ($6.1 K vs. $5.8 K, p = 0.10). The annualized total cost reduction per patient in the IG vs. CG was 20% (3.5 K, $17.7 K vs. $14.2 K, p = 0.04). Predictive analytics coupled with tailored interventions has great potential to reduce healthcare costs in older adults, thereby supporting population health management in home or community settings.
Collapse
Affiliation(s)
| | - Sara Bersche Golas
- Partners Connected Health Innovation, Partners HealthCare, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
| | | | - Ramya S Palacholla
- Partners Connected Health Innovation, Partners HealthCare, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Tufts University School of Medicine, Boston, MA, USA
| | | | | | - Joseph Kvedar
- Partners Connected Health Innovation, Partners HealthCare, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| |
Collapse
|