Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore

Ng, S Y; Rahman, Nabilah; Ang, Ian Yi Han; Sridharan, Srinivasan; Ramachandran, Sravan; Wang, Debby D.; Khoo, Astrid; Tan, Chuen Seng; Feng, Mengling; Toh, Sue‐Anne; Tan, Xin Quan

doi:10.1136/bmjopen-2019-031622

Cited by 10 publications

(12 citation statements)

References 46 publications

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“…In addition, an estimated 560,000 (18.6% prevalence) Singapore residents in 2015 have prediabetes, of whom an estimated 490,000 (16.2% prevalence) were undetected. Type 2 diabetes has been identified as a chronic disease whose patients persistently incur high health care costs [ 3 ]. Effective scalable prevention measures are thus urgently needed to prevent and better manage type 2 diabetes to reduce its burden.…”

Section: Introductionmentioning

confidence: 99%

A Personalized Mobile Health Program for Type 2 Diabetes During the COVID-19 Pandemic: Single-Group Pre–Post Study

Ang¹,

Tan²,

Tan³

et al. 2021

JMIR Diabetes

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Background With increasing type 2 diabetes prevalence, there is a need for effective programs that support diabetes management and improve type 2 diabetes outcomes. Mobile health (mHealth) interventions have shown promising results. With advances in wearable sensors and improved integration, mHealth programs could become more accessible and personalized. Objective The study aimed to evaluate the feasibility, acceptability, and effectiveness of a personalized mHealth-anchored intervention program in improving glycemic control and enhancing care experience in diabetes management. The program was coincidentally implemented during the national-level lockdown for COVID-19 in Singapore, allowing for a timely study of the use of mHealth for chronic disease management. Methods Patients with type 2 diabetes or prediabetes were enrolled from the Singapore Armed Forces and offered a 3-month intervention program in addition to the usual care they received. The program was standardized to include (1) in-person initial consultation with a clinical dietitian; (2) in-person review with a diabetes specialist doctor; (3) 1 continuous glucose monitoring device; (4) access to the mobile app for dietary intake and physical activity tracking, and communication via messaging with the dietitian and doctor; and (5) context-sensitive digital health coaching over the mobile app. Medical support was rendered to the patients on an as-needed basis when they required advice on adjustment of medications. Measurements of weight, height, and glycated hemoglobin A1c (HbA1c) were conducted at 2 in-person visits at the start and end of the program. At the end of the program, patients were asked to complete a short acceptability feedback survey to understand the motivation for joining the program, their satisfaction, and suggestions for improvement. Results Over a 4-week recruitment period, 130 individuals were screened, the enrollment target of 30 patients was met, and 21 patients completed the program and were included in the final analyses; 9 patients were lost to follow-up (full data were not available for the final analyses). There were no differences in the baseline characteristics between patients who were included and excluded from the final analyses (age category: P=.23; gender: P=.21; ethnicity: P>.99; diabetes status category: P=.52, medication adjustment category: P=.65; HbA1c category: P=.69; BMI: P>.99). The 21 patients who completed the study rated a mean of 9.0 out of 10 on the Likert scale for both satisfaction questions. For the Yes-No question on benefit of the program, all of the patients selected “Yes.” Mean HbA1c decreased from 7.6% to 7.0% (P=.004). There were no severe hypoglycemia events (glucose level <3.0 mmol/L) reported. Mean weight decreased from 76.8 kg to 73.9 kg (P<.001), a mean decrease of 3.5% from baseline weight. Mean BMI decreased from 27.8 kg/m2 to 26.7 kg/m2 (P<.001). Conclusions The personalized mHealth program was feasible, acceptable, and produced significant reductions in HbA1c (P=.004) and body weight (P<.001) in individuals with type 2 diabetes. Such mHealth programs could overcome challenges posed to chronic disease management by COVID-19, including disruptions to in-person health care access.

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Section: Introductionmentioning

confidence: 99%

A Personalized Mobile Health Program for Type 2 Diabetes During the COVID-19 Pandemic: Single-Group Pre–Post Study

Ang¹,

Tan²,

Tan³

et al. 2021

JMIR Diabetes

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“…For the topic of health care utilization and costs, 24 articles were included in this review. Most were retrospective cohort studies, 75 76 77 78 79 80 81 82 83 84 85 86 87 while six used prospective cohort studies, 88 89 90 91 92 93 four used a cross-sectional design, 94 95 96 97 and one used a survey for primary data collection. 98 Most studies used the EHR and administrative databases to collect data but three used surveys, 87 96 98 two used public datasets, 78 95 one used mobile phone data, 97 one used images, 90 and one used data from social media.…”

Section: Resultsmentioning

confidence: 99%

“…92 All studies were adult based with the exception of one study examining families. 98 Most studies were based in the United States, with the exception of three from Singapore, 83 85 93 one from China, 80 one from Brazil, 77 one from Italy, 88 one from Canada, 90 and one from the United Kingdom. 87 Sample sizes ranged from 190 to 780,295 observations.…”

Section: Resultsmentioning

confidence: 99%

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

et al. 2022

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Background The term “data science” encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. Objectives This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. Methods We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care–acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. Results Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. Conclusion This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

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“…On the other hand, some studies specifically focussed on patients with a temporary HNHC status such as Transient High Utilisers (THUs) in the study by Ng et al or cost bloomers in the study by Tamang et al Predicting temporary HNHC status may be particularly difficult but may also provide understanding in the difference between these two populations. 38,46 Model performance varied and was not always described in detail. Most studies (n = 45; 75%) conducted validation, yet this was external in only 20 studies (33%) limiting implications for clinical applicability.…”

Section: Discussionmentioning

confidence: 99%

“…2). 37,38 External validation of one of these models (HRUPoRT: High Resource User Population Risk Tool) in a separate study demonstrated strong discriminative ability (C-statistic 0.83) and good calibration (K in Fig. 2).…”

Section: Applicability For Clinical Usementioning

confidence: 99%

Prediction Models for Future High-Need High-Cost Healthcare Use: a Systematic Review

et al. 2022

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Background In an effort to improve both quality of care and cost-effectiveness, various care-management programmes have been developed for high-need high-cost (HNHC) patients. Early identification of patients at risk of becoming HNHC (i.e. case finding) is crucial to a programme’s success. We aim to systematically identify prediction models predicting future HNHC healthcare use in adults, to describe their predictive performance and to assess their applicability. Methods Ovid MEDLINE® All, EMBASE, CINAHL, Web of Science and Google Scholar were systematically searched from inception through January 31, 2021. Risk of bias and methodological quality assessment was performed through the Prediction model Risk Of Bias Assessment Tool (PROBAST). Results Of 5890 studies, 60 studies met inclusion criteria. Within these studies, 313 unique models were presented using a median development cohort size of 20,248 patients (IQR 5601–174,242). Predictors were derived from a combination of data sources, most often claims data (n = 37; 62%) and patient survey data (n = 29; 48%). Most studies (n = 36; 60%) estimated patients’ risk to become part of some top percentage of the cost distribution (top-1–20%) within a mean time horizon of 16 months (range 12–60). Five studies (8%) predicted HNHC persistence over multiple years. Model validation was performed in 45 studies (76%). Model performance in terms of both calibration and discrimination was reported in 14 studies (23%). Overall risk of bias was rated as ‘high’ in 40 studies (67%), mostly due to a ‘high’ risk of bias in the subdomain ‘Analysis’ (n = 37; 62%). Discussion This is the first systematic review (PROSPERO CRD42020164734) of non-proprietary prognostic models predicting HNHC healthcare use. Meta-analysis was not possible due to heterogeneity. Most identified models estimated a patient’s risk to incur high healthcare expenditure during the subsequent year. However, case-finding strategies for HNHC care-management programmes are best informed by a model predicting HNHC persistence. Therefore, future studies should not only focus on validating and extending existing models, but also concentrate on clinical usefulness.

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Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore

Cited by 10 publications

References 46 publications

A Personalized Mobile Health Program for Type 2 Diabetes During the COVID-19 Pandemic: Single-Group Pre–Post Study

A Personalized Mobile Health Program for Type 2 Diabetes During the COVID-19 Pandemic: Single-Group Pre–Post Study

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

Prediction Models for Future High-Need High-Cost Healthcare Use: a Systematic Review

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