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User engagement is key to the effectiveness of digital mental health interventions. Considerable research has examined the clinical outcomes of overall engagement with mental health apps (eg, frequency and duration of app use). However, few studies have examined how specific app use behaviors can drive change in outcomes. Understanding the clinical outcomes of more nuanced app use could inform the design of mental health apps that are more clinically effective to users. This study aimed to classify user behaviors in a suite of mental health apps and examine how different types of app use are related to depression and anxiety outcomes. We also compare the clinical outcomes of specific types of app use with those of generic app use (ie, intensity and duration of app use) to understand what aspects of app use may drive symptom improvement. We conducted a secondary analysis of system use data from an 8-week randomized trial of a suite of 13 mental health apps. We categorized app use behaviors through a mixed methods analysis combining qualitative content analysis and principal component analysis. Regression analyses were used to assess the association between app use and levels of depression and anxiety at the end of treatment. A total of 3 distinct clusters of app use behaviors were identified: learning, goal setting, and self-tracking. Each specific behavior had varied effects on outcomes. Participants who engaged in self-tracking experienced reduced depression symptoms, and those who engaged with learning and goal setting at a moderate level (ie, not too much or not too little) also had an improvement in depression. Notably, the combination of these 3 types of behaviors, what we termed \"clinically meaningful use,\" accounted for roughly the same amount of variance as explained by the overall intensity of app use (ie, total number of app use sessions). This suggests that our categorization of app use behaviors succeeded in capturing app use associated with better outcomes. However, anxiety outcomes were neither associated with specific behaviors nor generic app use. This study presents the first granular examination of user interactions with mental health apps and their effects on mental health outcomes. It has important implications for the design of mobile health interventions that aim to achieve greater user engagement and improved clinical efficacy.

The US health system has recently achieved widespread adoption of electronic health record (EHR) systems, primarily driven by financial incentives provided by the Meaningful Use (MU) program. Although successful in promoting EHR adoption and use, the program, and other contributing factors, also produced important unintended consequences (UCs) with far-reaching implications for the US health system. Based on our own experiences from large health information technology (HIT) adoption projects and a collection of key studies in HIT evaluation, we discuss the most prominent UCs of MU: failed expectations, EHR market saturation, innovation vacuum, physician burnout, and data obfuscation. We identify challenges resulting from these UCs and provide recommendations for future research to empower the broader medical and informatics communities to realize the full potential of a now digitized health system. We believe that fixing these unanticipated effects will demand efforts from diverse players such as health care providers, administrators, HIT vendors, policy makers, informatics researchers, funding agencies, and outside developers; promotion of new business models; collaboration between academic medical centers and informatics research departments; and improved methods for evaluations of HIT.

Unnecessary duplicate laboratory testing is common and costly. Systems-based means to avert unnecessary testing should be investigated and employed. We compared the effectiveness and cost savings associated with two clinical decision support tools to stop duplicate testing. The Hard Stop required telephone contact with the laboratory and justification to have the duplicate test performed, whereas the Smart Alert allowed the provider to bypass the alert at the point of order entry without justification. The Hard Stop alert was significantly more effective than the Smart Alert (92.3% vs 42.6%, respectively; P < .0001). The cost savings realized per alert activation was $16.08/alert for the Hard Stop alert vs $3.52/alert for the Smart Alert. Structural and process changes that require laboratory contact and justification for duplicate testing are more effective than interventions that allow providers to bypass alerts without justification at point of computerized physician order entry.

Policy makers are actively promoting the electronic exchange of health information to improve the quality and efficiency of health care. We conducted a national survey of organizations facilitating health information exchange, to assess national progress. We found that 30 percent of hospitals and 10 percent of ambulatory practices now participate in one of the 119 operational health information exchange efforts across the United States, substantial growth from prior surveys. However, we also found that 74 percent of health information exchange efforts report struggling to develop a sustainable business model. Our findings suggest that despite progress, there is a substantial risk that many current efforts to promote health information exchange will fail when public funds supporting these initiatives are depleted. [PUBLICATION ABSTRACT]

The US health care system is in the midst of an enormous change in the way health care providers and hospitals document, monitor, and share information about health and care delivery. Part of this transition involves a wholesale, but currently uneven, shift from paper-based records to electronic health record (EHR) systems. We used the most recent longitudinal survey of US hospitals to track how they are adopting and using EHR systems. Only 44 percent of hospitals report having and using what we define as at least a basic EHR system. And although 42.2 percent meet all of the federal stage 1 \"meaningful-use\" criteria, only 5.1 percent could meet the broader set of stage 2 criteria. Large urban hospitals continue to outpace rural and nonteaching hospitals in adopting EHR systems. The increase in adoption overall suggests that the positive and negative financial incentives currently in place across the US health care system are working as intended. However, achieving a nationwide health information technology infrastructure may require efforts targeted at smaller and rural hospitals. [PUBLICATION ABSTRACT]

Background: Adoption and implementation of electronic health records (EHRs) has not been without challenges as it infuses technology into what has been a historically manual process of recording patient information. In an effort to identify these challenges, the Office of the National Coordinator for Health Information Technology leveraged the Regional Extension Center population of over 140,000 providers to develop a structured way to track challenges to EHR adoption and Meaningful Use (MU). Objectives: This report summarizes challenges to EHR adoption and MU based on nationwide data supplied by 55 Regional Extension Centers reporting over 19,000 issues representing over 43,000 unique health care providers. Practices were grouped on the basis of their place in the lifecycle of EHR adoption and MU achievement. Results: Provider engagement and administrative issues were among the more common issues reported across all cohorts. The most challenging MU measure was the clinical summaries measure, but MU Measure challenges varied by practice setting. Conclusions: EHR adoption and MU challenges are unique to practice setting and stage of the adoption process.

The HITECH Act created incentives to encourage adoption of electronic health records. As of May 2012, only 12.2% of 62,226 eligible professionals had attested to meaningful use, including 9.8% of specialists and 17.8% of primary care providers. To the Editor: In 2009, the Health Information Technology for Economic and Clinical Health (HITECH) Act established Medicare and Medicaid incentive programs to encourage the adoption of electronic health records (EHRs) by hospitals and eligible professionals. Under Medicare, eligible professionals who show “meaningful use” of certified EHRs are eligible for payments up to $44,000, whereas eligible professionals who do not are subject to penalties after 2015. 1 , 2 Stage 1 requirements for meaningful use involve the use of key EHR functions, including electronic prescribing, drug–drug and drug–allergy checking, and the maintenance of problem, medication, and allergy lists. In stage 1, providers . . .

Expanding the use of interoperable electronic health record (EHR) systems to improve health care delivery is a national policy priority. We used the 2010-12 National Ambulatory Medical Care Survey-Electronic Health Records Survey to examine which physicians in what types of practices are implementing the systems, and how they are using them. We found that 72 percent of physicians had adopted some type of system and that 40 percent had adopted capabilities required for a basic EHR system. The highest relative increases in adoption were among physicians with historically low adoption levels, including older physicians and those working in solo practices or community health centers. As of 2012, physicians in rural areas had higher rates of adoption than those in large urban areas, and physicians in counties with high rates of poverty had rates of adoption comparable to those in areas with less poverty. However, small practices continued to lag behind larger practices. Finally, the majority of physicians who adopted the EHR capabilities required to obtain federal financial incentives used the capabilities routinely, with few differences across physician groups. [PUBLICATION ABSTRACT]

ABSTRACT BACKGROUND Federal initiatives are underway that provide physicians with financial incentives for meaningful use (MU) of electronic health records (EHRs) and assistance to purchase and implement EHRs. OBJECTIVE We sought to examine readiness and interest in MU among primary care physicians and specialists, and identify factors that may affect their readiness to obtain MU incentives. DESIGN/PARTICIPANTS We analyzed 4 years of data (2008–2011) from the National Ambulatory Medical Care Survey (NAMCS) Electronic Medical Record (EMR) Supplement, an annual cross-sectional nationally representative survey of non-federally employed office-based physicians. MAIN MEASURES Survey-weighted EHR adoption rates, potential to meet selected MU criteria, and self-reported intention to apply for MU incentives. We also examined the association between physician and practice characteristics and readiness for MU. KEY RESULTS The overall sample consisted of 10,889 respondents, with weighted response rates of 62 % (2008); 74 % (2009); 66 % (2010); and 61 % (2011). Primary care physicians’ adoption of EHRs with the potential to meet MU nearly doubled from 2009 to 2011 (18 % to 38 %, p  < 0.01), and was significantly higher than specialists (19 %) in 2011 ( p  < 0.01). In 2011, half of physicians (52 %) expressed their intention to apply for MU incentives; this did not vary by specialty. Multivariate analyses report that EHR adoption was significantly higher in both 2010 and 2011 compared to 2009, and primary care physicians and physicians working in larger or multi-specialty practices or for HMOs were more likely to adopt EHRs with the potential to meet MU. CONCLUSIONS Physician EHR adoption rates increased in advance of MU incentive payments. Although interest in MU incentives did not vary by specialty, primary care physicians had significantly higher rates of adopting EHRs with the potential to meet MU. Addressing barriers to EHR adoption, which may vary by specialty, will be important to enhancing coordination of care.

The 2009 Health Information Technology for Economic and Clinical Health Act sets three stages of Meaningful Use requirements for the electronic health records incentive program. Health information exchange (HIE) technologies are critical in the meaningful use of electronic health records to support patient care coordination. However, HIE use trends and barriers remain unclear across hospitals in South Carolina (SC), a state with the earliest HIE implementation. This study aims to explore changes in the proportion of HIE participation and factors associated with HIE participation, and barriers to exchange and interoperability across SC hospitals. This study derived data from a longitudinal data set of the 2014-2020 American Hospital Association Information Technology Supplement for 69 SC hospitals. The primary outcome was whether a hospital participated in HIE in a year. A cross-sectional multivariable logistic regression model, clustered at the hospital level and weighted by bed size, was used to identify factors associated with HIE participation. The second outcome was barriers to sending, receiving, or finding patient health information to or from other organizations or hospital systems. The frequency of hospitals reporting each barrier related to exchange and interoperability were then calculated. Hospitals in SC have been increasingly participating in HIE, improving from 43% (24/56) in 2014 to 82% (54/66) in 2020. After controlling for other hospital factors, teaching hospitals (adjusted odds ratio [AOR] 3.7, 95% CI 1.0-13.3), system-affiliated hospitals (AOR 6.6, 95% CI 3.2-13.7), and rural referral hospitals (AOR 8.0, 95% CI 1.2-53.4) had higher odds to participate in HIE than their counterparts, whereas critical access hospitals (AOR 0.1, 95% CI 0.02-0.6) were less likely to participate in HIE than their counterparts reimbursed by the prospective payment system. Hospitals with greater ratios of Medicare or Medicaid inpatient days to total inpatient days also reported higher odds of HIE participation. Despite the majority of hospitals reporting HIE participation in 2020, barriers to exchange and interoperability remained, including lack of provider contacts (27/40, 68%), difficulty in finding patient health information (27/40, 68%), adapting different vendor platforms (26/40, 65%), difficulty matching or identifying same patients between systems (23/40, 58%), and providers that do not typically exchange patient data (23/40, 58%). HIE participation has been widely adopted in SC hospitals. Our findings highlight the need to incentivize optimization of HIE and seamless information exchange by facilitating and implementing standardization of health information across various HIE systems and by addressing other technical issues, including providing providers' addresses and training HIE stakeholders to find relevant information. Policies and efforts should include more collaboration with vendors to reduce platform compatibility issues and more user engagement and technical training and support to facilitate effective, accurate, and efficient exchange of provider contacts and patient health information.