Push or pull? Digital notification platform implementation reduces dysglycaemia.

Pushing selected information to clinicians, as opposed to the traditional method of clinicians pulling information from an electronic medical record, has the potential to improve care. A digital notification platform was designed by clinicians and implemented in a tertiary hospital to flag dysglycaemia. There were 112 patients included in the study, and the post-implementation group demonstrated lower rates of dysglycaemia (2.5% vs 1.1%, P = 0.038). These findings raise considerations for information delivery methods for multiple domains in contemporary healthcare.

The future is bright: artificial intelligence for trainee medical officers in Australia and New Zealand.

Given their frontline role in Australia and Aotearoa New Zealand (ANZ) healthcare, trainee medical officers (TMOs) will play a crucial role in the development and use of artificial intelligence (AI) for clinical care, ongoing medical education and research. As 'digital natives', particularly those with technical expertise in AI, TMOs should also be leaders in informing the safe uptake and governance of AI within ANZ healthcare as they have a practical understanding of its associated risks and benefits. However, this is only possible if a culture of broad collaboration is instilled while the use of AI in ANZ is still in its initial phase.

Cashing in: cost-benefit analysis framework for digital hospitals.

BACKGROUND: For many countries, especially those outside the USA without incentive payments, implementing and maintaining electronic medical records (EMR) is expensive and can be controversial given the large amounts of investment. Evaluating the value of EMR implementation is necessary to understand whether or not, such investment, especially when it comes from the public source, is an efficient allocation of healthcare resources. Nonetheless, most countries have struggled to measure the return on EMR investment due to the lack of appropriate evaluation frameworks. METHODS: This paper outlines the development of an evidence-based digital health cost-benefit analysis (eHealth-CBA) framework to calculate the total economic value of the EMR implementation over time. A net positive benefit indicates such investment represents improved efficiency, and a net negative is considered a wasteful use of public resources. RESULTS: We developed a three-stage process that takes into account the complexity of the healthcare system and its stakeholders, the investment appraisal and evaluation practice, and the existing knowledge of EMR implementation. The three stages include (1) literature review, (2) stakeholder consultation, and (3) CBA framework development. The framework maps the impacts of the EMR to the quadruple aim of healthcare and clearly creates a method for value assessment. CONCLUSIONS: The proposed framework is the first step toward developing a comprehensive evaluation framework for EMRs to inform health decision-makers about the economic value of digital investments rather than just the financial value.

Perceived Impact of Digital Health Maturity on Patient Experience, Population Health, Health Care Costs, and Provider Experience: Mixed Methods Case Study.

BACKGROUND: Health care organizations understand the importance of new technology implementations; however, the best strategy for implementing successful digital transformations is often unclear. Digital health maturity assessments allow providers to understand the progress made toward technology-enhanced health service delivery. Existing models have been criticized for their lack of depth and breadth because of their technology focus and neglect of meaningful outcomes. OBJECTIVE: We aimed to examine the perceived impacts of digital health reported by health care staff employed in health care organizations across a spectrum of digital health maturity. METHODS: A mixed methods case study was conducted. The digital health maturity of public health care systems (n=16) in Queensland, Australia, was examined using the quantitative Digital Health Indicator (DHI) self-assessment survey. The lower and upper quartiles of DHI scores were calculated and used to stratify sites into 3 groups. Using qualitative methods, health care staff (n=154) participated in interviews and focus groups. Transcripts were analyzed assisted by automated text-mining software. Impacts were grouped according to the digital maturity of the health care worker's facility and mapped to the quadruple aims of health care: improved patient experience, improved population health, reduced health care cost, and enhanced provider experience. RESULTS: DHI scores ranged between 78 and 193 for the 16 health care systems. Health care systems in the high-maturity category (n=4, 25%) had a DHI score of ≥166.75 (the upper quartile); low-maturity sites (n=4, 25%) had a DHI score of ≤116.75 (the lower quartile); and intermediate-maturity sites (n=8, 50%) had a DHI score ranging from 116.75 to 166.75 (IQR). Overall, 18 perceived impacts were identified. Generally, a greater number of positive impacts were reported in health care systems of higher digital health maturity. For patient experiences, higher maturity was associated with maintaining a patient health record and tracking patient experience data, while telehealth enabled access and flexibility across all digital health maturity categories. For population health, patient journey tracking and clinical risk mitigation were reported as positive impacts at higher-maturity sites, and telehealth enabled health care access and efficiencies across all maturity categories. Limited interoperability and organizational factors (eg, strategy, policy, and vision) were universally negative impacts affecting health service delivery. For health care costs, the resource burden of ongoing investments in digital health and a sustainable skilled workforce was reported. For provider experiences, the negative impacts of poor usability and change fatigue were universal, while network and infrastructure issues were negative impacts at low-maturity sites. CONCLUSIONS: This is one of the first studies to show differences in the perceived impacts of digital maturity of health care systems at scale. Higher digital health maturity was associated with more positive reported impacts, most notably in achieving outcomes for the population health aim.

Impact of digital health on the quadruple aims of healthcare: A correlational and longitudinal study (Digimat Study).

BACKGROUND: Digital healthcare aims to deliver on the quadruple aim: enhance patient experiences, improve population health, reduce costs and improve provider experiences. Despite large investments, it is unclear how advancing digital health enables these healthcare aims. OBJECTIVE: Our objectives were to: 1) measure the correlation between digital capability and health system outcomes mapped to the quadruple aim, and 2) measure the longitudinal impact of electronic medical record implementations upon health system outcomes. MATERIALS AND METHODS: We undertook two studies: 1) Digital health correlational study investigating the association among healthcare system capability and healthcare aims, and 2) Digital hospital longitudinal study investigating outcomes pre and post electronic medical record implementation. RESULTS: Digital health capability was associated with lower staff turnover. Digitising healthcare services was associated with decreased medication errors, decreased nosocomial infections, increased hospital activity, and a transient increase in staff leave. DISCUSSION: These results suggest positive impacts on the population health and healthcare costs aim, minimal impacts on the provider experience aim and no observed impacts to the patient experience aim. CONCLUSION: These findings should provide confidence to healthcare decision-makers investing in digital health.

Translational artificial intelligence-led optimization and realization of estimated discharge with a supportive weekend interprofessional flow team (TAILORED-SWIFT).

Weekend discharges occur less frequently than discharges on weekdays, contributing to hospital congestion. Artificial intelligence algorithms have previously been derived to predict which patients are nearing discharge based upon ward round notes. In this implementation study, such an artificial intelligence algorithm was coupled with a multidisciplinary discharge facilitation team on weekend shifts. This approach was implemented in a tertiary hospital, and then compared to a historical cohort from the same time the previous year. There were 3990 patients included in the study. There was a significant increase in the proportion of inpatients who received weekend discharges in the intervention group compared to the control group (median 18%, IQR 18-20%, vs median 14%, IQR 12% to 17%, P = 0.031). There was a corresponding higher absolute number of weekend discharges during the intervention period compared to the control period (P = 0.025). The studied intervention was associated with an increase in weekend discharges and economic analyses support this approach as being cost-effective. Further studies are required to examine the generalizability of this approach to other centers.

Adverse childhood experiences, the risk of pregnancy complications and adverse pregnancy outcomes: a systematic review and meta-analysis.

BACKGROUND: Adverse childhood experiences (ACEs) have a profound negative impact on health. However, the strength of the association between ACEs and pregnancy complications and adverse pregnancy outcomes is not well quantified or understood. OBJECTIVE: To conduct a systematic review and meta-analysis of the association between ACEs and risk of pregnancy complications and adverse pregnancy outcomes. SEARCH STRATEGY: A comprehensive search was conducted using PubMed, Embase, CINAHL, PsycINFO, ClinicalTrials.gov and Google scholar up to July 2022. DATA COLLECTION AND ANALYSIS: Two reviewers independently conducted the screening and quality appraisal using a validated tool. Meta-analysis using the quality-effects model on the reported odds ratio (OR) was conducted. Heterogeneity and inconsistency were examined using the I2 statistics. RESULTS: 32 studies from 1508 met a priori inclusion criteria for systematic review, with 21 included in the meta-analysis. Pooled analyses showed that exposure to ACEs increased the risk of pregnancy complications (OR 1.37, 95% CI 1.20 to 1.57) and adverse pregnancy outcomes (OR 1.31, 95% CI 1.17 to 1.47). In sub-group analysis, maternal ACEs were associated with gestational diabetes mellitus (OR 1.39, 95% CI 1.11 to 1.74), antenatal depression (OR 1.59, 95% CI 1.15 to 2.20), low offspring birth weight (OR 1.27, 95% CI 1.02 to 1.47), and preterm delivery (OR 1.41, 95% CI 1.16 to 1.71). CONCLUSION: The results suggest that exposure to ACEs increases the risk of pregnancy complications and adverse pregnancy outcomes. Preventive strategies, screening and trauma-informed care need to be examined to improve maternal and child health.

Evaluating Digital Health Capability at Scale Using the Digital Health Indicator.

BACKGROUND: Health service providers must understand their digital health capability if they are to drive digital transformation in a strategic and informed manner. Little is known about the assessment and benchmarking of digital maturity or capability at scale across an entire jurisdiction. The public health care system across the state of Queensland, Australia has an ambitious 10-year digital transformation strategy. OBJECTIVE: The aim of this research was to evaluate the digital health capability in Queensland to inform digital health strategy and investment. METHODS: The Healthcare Information and Management Systems Society Digital Health Indicator (DHI) was used via a cross-sectional survey design to assess four core dimensions of digital health transformation: governance and workforce; interoperability; person-enabled health; and predictive analytics across an entire jurisdiction simultaneously. The DHI questionnaire was completed by each health care system (n = 16) within Queensland in February to July 2021. DHI is scored 0 to 400 and dimension score is 0 to 100. RESULTS: The results reveal a variation in DHI scores reflecting the diverse stages of health care digitization across the state. The average DHI score across sites was 143 (range 78-193; SD35.3) which is similar to other systems in the Oceania region and global public systems but below the global private average. Governance and workforce was on average the highest scoring dimension (x̅= 54), followed by interoperability (x̅ = 46), person-enabled health (x̅ = 36), and predictive analytics (x̅ = 30). CONCLUSION: The findings were incorporated into the new digital health strategy for the jurisdiction. As one of the largest single simultaneous assessments of digital health capability globally, the findings and lessons learnt offer insights for policy makers and organizational managers.