Investigating the coverage of the Arkansas All-Payer Claims Database for examining health disparities related to persistent poverty areas in colorectal cancer patients.

PURPOSE: We aimed to (1) determine the extent of coverage of colorectal cancer patients in Arkansas All-Payer Claims Database (APCD), (2) assess coverage difference between persistent poverty and other areas, and (3) identify patient, tumor, and area factors associated with inclusion in APCD. METHODS: Data were from 2018 to 2020 Arkansas APCD linked with 2019 Arkansas Central Cancer Registry (ACCR). We constructed four cohorts to assess APCD's coverage of CRC patients: (Cohort 1) ≥ 1 day of medical coverage in APCD in 2019; (Cohort 2) APCD coverage in the diagnosis month; continuous APCD coverage in the 30; Year around diagnosis (six months before to five months after diagnosis month) (Cohort 3); or until death within six months (Cohort 4). We compared proportions in the cohorts by area persistent poverty designation. Logistic regressions identified factors associated with inclusion in APCD cohorts. PATIENT SELECTION: CRC patients diagnosed in 2019 from ACCR, excluding in situ disease. RESULTS: Of the 1,510 CRC patients diagnosed in 2019, 83% had ≥ 1 day of medical coverage in 2019 APCD (Cohort1), 81% had coverage in the diagnosis month (Cohort 2), and 63% had continuous coverage in the year around diagnosis (Cohort 3). Additionally, 11% died within six months but had continuous coverage until death (Cohort 4, 74%). No coverage difference was found between persist poverty and other areas. Age and primary payer type at diagnosis were the main predictors of inclusion in APCD. CONCLUSION: Arkansas APCD had high coverage of Arkansas CRC patients. No selection bias by area of persistent poverty designation was present.

A comparative study of home healthcare quality in urban and rural home health agencies throughout the USA (2010-22).

Urban-rural disparities in medical care, including in home healthcare, persist globally. With aging populations and medical advancements, demand for home health services rises, warranting investigation into home healthcare disparities. Our study aimed to (i) investigate the impact of rurality on home healthcare quality, and (ii) assess the temporal disparities and the changes in disparities in home healthcare quality between urban and rural home health agencies (HHAs), incorporating an analysis of geospatial distribution to visualize the underlying patterns. This study analyzed data from HHAs listed on the Centers for Medicare and Medicaid Services website, covering the period from 2010 to 2022. Data were classified into urban and rural categories for each HHA. We employed panel data analysis to examine the impact of rurality on home healthcare quality, specifically focusing on hospital admission and emergency room (ER) visit rates. Disparities between urban and rural HHAs were assessed using the Wilcoxon test, with results visualized through line and dot plots and heat maps to illustrate trends and differences comprehensively. Rurality is demonstrated as the most significant variable in hospital admission and ER visit rates in the panel data analysis. Urban HHAs consistently exhibit significantly lower hospital admission rates and ER visit rates compared to rural HHAs from 2010 to 2022. Longitudinally, the gap in hospital admission rates between urban and rural HHAs is shrinking, while there is an increasing gap in ER visit rates. In 2022, HHAs in Mountain areas, which are characterized by a higher proportion of rural regions, exhibited higher hospital admission and ER visit rates than other areas. This study underscores the persistent urban-rural disparities in home healthcare quality. The analysis emphasizes the ongoing need for targeted interventions to address disparities in home healthcare delivery and ensure equitable access to quality care across urban and rural regions. Our findings have the potential to inform policy and practice, promoting equity and efficiency in the long-term care system, for better health outcomes throughout the USA.

Public Maternal Health Dashboards in the United States: Descriptive Assessment.

BACKGROUND: Data dashboards have become more widely used for the public communication of health-related data, including in maternal health. OBJECTIVE: We aimed to evaluate the content and features of existing publicly available maternal health dashboards in the United States. METHODS: Through systematic searches, we identified 80 publicly available, interactive dashboards presenting US maternal health data. We abstracted and descriptively analyzed the technical features and content of identified dashboards across four areas: (1) scope and origins, (2) technical capabilities, (3) data sources and indicators, and (4) disaggregation capabilities. Where present, we abstracted and qualitatively analyzed dashboard text describing the purpose and intended audience. RESULTS: Most reviewed dashboards reported state-level data (58/80, 72%) and were hosted on a state health department website (48/80, 60%). Most dashboards reported data from only 1 (33/80, 41%) or 2 (23/80, 29%) data sources. Key indicators, such as the maternal mortality rate (10/80, 12%) and severe maternal morbidity rate (12/80, 15%), were absent from most dashboards. Included dashboards used a range of data visualizations, and most allowed some disaggregation by time (65/80, 81%), geography (65/80, 81%), and race or ethnicity (55/80, 69%). Among dashboards that identified their audience (30/80, 38%), legislators or policy makers and public health agencies or organizations were the most common audiences. CONCLUSIONS: While maternal health dashboards have proliferated, their designs and features are not standard. This assessment of maternal health dashboards in the United States found substantial variation among dashboards, including inconsistent data sources, health indicators, and disaggregation capabilities. Opportunities to strengthen dashboards include integrating a greater number of data sources, increasing disaggregation capabilities, and considering end-user needs in dashboard design.

Identifying the process and agency characteristics associated with poor utilization outcomes in home healthcare.

This study identified the process and agency characteristics associated with poor utilization outcomes - higher percentages of patients (i) admitted to an acute care organization and (ii) visited an emergency room (ER) unplanned without hospitalization - for home health agencies (HHAs) in the United States. We conducted a secondary analysis of data about HHAs' various characteristics, process adherence levels, and utilization outcomes collected from disparate public repositories for 2010-2022. We developed descriptive tree-based models using HHAs' hospital admission or ER visit percentages as response variables. Across the board, hospital admission percentages have steadily improved while ER percentages deteriorated for an extended period. Recently, checking for fall risks and depression was associated with improved outcomes for urban agencies. In general, rural HHAs had worse utilization outcomes than urban HHAs. Targeted investments and improvement initiatives can help rural HHAs close the urban-rural gap in the future.

Technology Innovations for Better Fall Risk Management in Home Care.

Achieving better fall risk management is an integral component of quality home care. The current qualitative study uncovers the challenges and opportunities of home health agencies (HHAs) in achieving better fall risk management. A secondary document analysis was adopted to learn from rich contextual information in fall incident reports recorded in a HHA. Poor engagement of patients and caregivers was a contributing factor in many fall incidents. Patients often fell as a result of not understanding or accepting their physical limitations. For better fall risk management, many incidents highlighted a need for providing complete and thorough care, better coordination of care, higher levels of sociocultural awareness, patient engagement, and caregiver involvement. The results provide evidence regarding the challenges and opportunities for improving fall risk management in home care along with insight about how information technology solutions can support improvement initiatives. [Journal of Gerontological Nursing, 44(7), 15-20.].

Supporting the information domains of fall-risk management in home care via health information technology.

In the United States, home care clinicians often start the episode of care devoid of relevant fall-risk information. By collecting and analyzing qualitative data from 30 clinicians in one home health agency, this case study aimed to understand how the currently adopted information technology solutions supported the clinicians' fall-risk management (FRM) information domains, and explored opportunities to adopt other solutions to better support FRM. The currently adopted electronic health record system and fall-reporting application served only some information domains with a limited capacity. Substantial improvement in addressing the FRM information domains is possible by effectively modifying the existing solutions and purposefully adopting new solutions.

The challenges and opportunities of continuous data quality improvement for healthcare administration data.

Background: Various data quality issues have prevented healthcare administration data from being fully utilized when dealing with problems ranging from COVID-19 contact tracing to controlling healthcare costs. Objectives: (i) Describe the currently adopted approaches and practices for understanding and improving the quality of healthcare administration data. (ii) Explore the challenges and opportunities to achieve continuous quality improvement for such data. Materials and Methods: We used a qualitative approach to obtain rich contextual data through semi-structured interviews conducted at a state health agency regarding Medicaid claims and reimbursement data. We interviewed all data stewards knowledgeable about the data quality issues experienced at the agency. The qualitative data were analyzed using the Framework method. Results: Sixteen themes emerged from our analysis, collected under 4 categories: (i) Defect characteristics: Data defects showed variability, frequently remained obscure, and led to negative outcomes. Detecting and resolving them was often difficult, and the work required often exceeded the organizational boundaries. (ii) Current process and people issues: The agency adopted primarily ad-hoc, manual approaches to resolving data quality problems leading to work frustration. (iii) Challenges: Communication and lack of knowledge about legacy software systems and the data maintained in them constituted challenges, followed by different standards used by various organizations and vendors, and data verification difficulties. (iv) Opportunities: Training, tool support, and standardization of data definitions emerged as immediate opportunities to improve data quality. Conclusions: Our results can be useful to similar agencies on their journey toward becoming learning health organizations leveraging data assets effectively and efficiently.

Transforming health and well-being through publishing computable biomedical knowledge (CBK).

Computable biomedical knowledge artifacts (CBKs) are software programs that transform input data into practical output. CBKs are expected to play a critical role in the future of learning health systems. While there has been rapid growth in the development of CBKs, broad adoption is hampered by limited verification, documentation, and dissemination channels. To address these issues, the Learning Health Systems journal created a track dedicated to publishing CBKs through a peer-review process. Peer review of CBKs should improve reproducibility, reuse, trust, and recognition in biomedical fields, contributing to learning health systems. This special issue introduces the CBK track with four manuscripts reporting a functioning CBK, and another four manuscripts tackling methodological, policy, deployment, and platform issues related to fostering a healthy ecosystem for CBKs. It is our hope that the potential of CBKs exemplified and highlighted by these quality publications will encourage scientists within learning health systems and related biomedical fields to engage with this new form of scientific discourse.

De-identification methods for open health data: the case of the Heritage Health Prize claims dataset.

BACKGROUND: There are many benefits to open datasets. However, privacy concerns have hampered the widespread creation of open health data. There is a dearth of documented methods and case studies for the creation of public-use health data. We describe a new methodology for creating a longitudinal public health dataset in the context of the Heritage Health Prize (HHP). The HHP is a global data mining competition to predict, by using claims data, the number of days patients will be hospitalized in a subsequent year. The winner will be the team or individual with the most accurate model past a threshold accuracy, and will receive a US $3 million cash prize. HHP began on April 4, 2011, and ends on April 3, 2013. OBJECTIVE: To de-identify the claims data used in the HHP competition and ensure that it meets the requirements in the US Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. METHODS: We defined a threshold risk consistent with the HIPAA Privacy Rule Safe Harbor standard for disclosing the competition dataset. Three plausible re-identification attacks that can be executed on these data were identified. For each attack the re-identification probability was evaluated. If it was deemed too high then a new de-identification algorithm was applied to reduce the risk to an acceptable level. We performed an actual evaluation of re-identification risk using simulated attacks and matching experiments to confirm the results of the de-identification and to test sensitivity to assumptions. The main metric used to evaluate re-identification risk was the probability that a record in the HHP data can be re-identified given an attempted attack. RESULTS: An evaluation of the de-identified dataset estimated that the probability of re-identifying an individual was .0084, below the .05 probability threshold specified for the competition. The risk was robust to violations of our initial assumptions. CONCLUSIONS: It was possible to ensure that the probability of re-identification for a large longitudinal dataset was acceptably low when it was released for a global user community in support of an analytics competition. This is an example of, and methodology for, achieving open data principles for longitudinal health data.

Categorizing metadata to help mobilize computable biomedical knowledge.

INTRODUCTION: Computable biomedical knowledge artifacts (CBKs) are digital objects conveying biomedical knowledge in machine-interpretable structures. As more CBKs are produced and their complexity increases, the value obtained from sharing CBKs grows. Mobilizing CBKs and sharing them widely can only be achieved if the CBKs are findable, accessible, interoperable, reusable, and trustable (FAIR+T). To help mobilize CBKs, we describe our efforts to outline metadata categories to make CBKs FAIR+T. METHODS: We examined the literature regarding metadata with the potential to make digital artifacts FAIR+T. We also examined metadata available online today for actual CBKs of 12 different types. With iterative refinement, we came to a consensus on key categories of metadata that, when taken together, can make CBKs FAIR+T. We use subject-predicate-object triples to more clearly differentiate metadata categories. RESULTS: We defined 13 categories of CBK metadata most relevant to making CBKs FAIR+T. Eleven of these categories (type, domain, purpose, identification, location, CBK-to-CBK relationships, technical, authorization and rights management, provenance, evidential basis, and evidence from use metadata) are evident today where CBKs are stored online. Two additional categories (preservation and integrity metadata) were not evident in our examples. We provide a research agenda to guide further study and development of these and other metadata categories. CONCLUSION: A wide variety of metadata elements in various categories is needed to make CBKs FAIR+T. More work is needed to develop a common framework for CBK metadata that can make CBKs FAIR+T for all stakeholders.

Findings From Severe Maternal Morbidity Surveillance and Review in Maryland.

Importance: In the US, more than 50 000 women experience severe maternal morbidity (SMM) each year, and the SMM rate more than doubled during the past 25 years. In response, professional organizations called for birthing facilities to routinely identify and review SMM events and identify prevention opportunities. Objective: To examine SMM levels, primary causes, and factors associated with the preventability of SMM using Maryland's SMM surveillance and review program. Design, Setting, and Participants: This cross-sectional study included pregnant and postpartum patients at 42 days or less after delivery who were hospitalized at 1 of 6 birthing hospitals in Maryland between August 1, 2020, and November 30, 2021. Hospital-based SMM surveillance was conducted through a detailed review of medical records. Exposures: Hospitalization during pregnancy or within 42 days post partum. Main Outcomes and Measures: The main outcomes were admission to an intensive care unit, having at least 4 U of red blood cells transfused, and/or having COVID-19 infection requiring inpatient hospital care. Results: A total of 192 SMM events were identified and reviewed. Patients with SMM had a mean [SD] age of 31 [6.49] years; 9 [4.7%] were Asian, 27 [14.1%] were Hispanic, 83 [43.2%] were non-Hispanic Black, and 68 [35.4%] were non-Hispanic White. Obstetric hemorrhage was the leading primary cause of SMM (83 [43.2%]), followed by COVID-19 infection (57 [29.7%]) and hypertensive disorders of pregnancy (17 [8.9%]). The SMM rate was highest among Hispanic patients (154.9 per 10 000 deliveries), primarily driven by COVID-19 infection. The rate of SMM among non-Hispanic Black patients was nearly 50% higher than for non-Hispanic White patients (119.9 vs 65.7 per 10 000 deliveries). The SMM outcome assessed could have been prevented in 61 events (31.8%). Clinician-level factors and interventions in the antepartum period were most frequently cited as potentially altering the SMM outcome. Practices that were performed well most often pertained to hospitals' readiness and adequate response to managing pregnancy complications. Recommendations for care improvement focused mainly on timely recognition and rapid response to such. Conclusions and Relevance: The findings of this cross-sectional study, which used hospital-based SMM surveillance and review beyond the mere exploration of administrative data, offers opportunities for identifying valuable quality improvement strategies to reduce SMM. Immediate strategies to reduce SMM in Maryland should target its most common causes and address factors associated with preventability identified at individual hospitals.

De-identifying a public use microdata file from the Canadian national discharge abstract database.

BACKGROUND: The Canadian Institute for Health Information (CIHI) collects hospital discharge abstract data (DAD) from Canadian provinces and territories. There are many demands for the disclosure of this data for research and analysis to inform policy making. To expedite the disclosure of data for some of these purposes, the construction of a DAD public use microdata file (PUMF) was considered. Such purposes include: confirming some published results, providing broader feedback to CIHI to improve data quality, training students and fellows, providing an easily accessible data set for researchers to prepare for analyses on the full DAD data set, and serve as a large health data set for computer scientists and statisticians to evaluate analysis and data mining techniques. The objective of this study was to measure the probability of re-identification for records in a PUMF, and to de-identify a national DAD PUMF consisting of 10% of records. METHODS: Plausible attacks on a PUMF were evaluated. Based on these attacks, the 2008-2009 national DAD was de-identified. A new algorithm was developed to minimize the amount of suppression while maximizing the precision of the data. The acceptable threshold for the probability of correct re-identification of a record was set at between 0.04 and 0.05. Information loss was measured in terms of the extent of suppression and entropy. RESULTS: Two different PUMF files were produced, one with geographic information, and one with no geographic information but more clinical information. At a threshold of 0.05, the maximum proportion of records with the diagnosis code suppressed was 20%, but these suppressions represented only 8-9% of all values in the DAD. Our suppression algorithm has less information loss than a more traditional approach to suppression. Smaller regions, patients with longer stays, and age groups that are infrequently admitted to hospitals tend to be the ones with the highest rates of suppression. CONCLUSIONS: The strategies we used to maximize data utility and minimize information loss can result in a PUMF that would be useful for the specific purposes noted earlier. However, to create a more detailed file with less information loss suitable for more complex health services research, the risk would need to be mitigated by requiring the data recipient to commit to a data sharing agreement.

Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions.

OBJECTIVE: Development of systematic approaches for understanding and assessing data quality is becoming increasingly important as the volume and utilization of health data steadily increases. In this study, a taxonomy of data defects was developed and utilized when automatically detecting defects to assess Medicaid data quality maintained by one of the states in the United States. MATERIALS AND METHODS: There were more than 2.23 million rows and 32 million cells in the Medicaid data examined. The taxonomy was developed through document review, descriptive data analysis, and literature review. A software program was created to automatically detect defects by using a set of constraints whose development was facilitated by the taxonomy. RESULTS: Five major categories and seventeen subcategories of defects were identified. The major categories are missingness, incorrectness, syntax violation, semantic violation, and duplicity. More than 3 million defects were detected indicating substantial problems with data quality. Defect density exceeded 10% in five tables. The majority of the data defects belonged to format mismatch, invalid code, dependency-contract violation, and implausible value types. Such contextual knowledge can support prioritized quality improvement initiatives for the Medicaid data studied. CONCLUSIONS: This research took the initial steps to understand the types of data defects and detect defects in large healthcare datasets. The results generally suggest that healthcare organizations can potentially benefit from focusing on data quality improvement. For those purposes, the taxonomy developed and the approach followed in this study can be adopted.

A survey of quality assurance practices in biomedical open source software projects.

BACKGROUND: Open source (OS) software is continuously gaining recognition and use in the biomedical domain, for example, in health informatics and bioinformatics. OBJECTIVES: Given the mission critical nature of applications in this domain and their potential impact on patient safety, it is important to understand to what degree and how effectively biomedical OS developers perform standard quality assurance (QA) activities such as peer reviews and testing. This would allow the users of biomedical OS software to better understand the quality risks, if any, and the developers to identify process improvement opportunities to produce higher quality software. METHODS: A survey of developers working on biomedical OS projects was conducted to examine the QA activities that are performed. We took a descriptive approach to summarize the implementation of QA activities and then examined some of the factors that may be related to the implementation of such practices. RESULTS: Our descriptive results show that 63% (95% CI, 54-72) of projects did not include peer reviews in their development process, while 82% (95% CI, 75-89) did include testing. Approximately 74% (95% CI, 67-81) of developers did not have a background in computing, 80% (95% CI, 74-87) were paid for their contributions to the project, and 52% (95% CI, 43-60) had PhDs. A multivariate logistic regression model to predict the implementation of peer reviews was not significant (likelihood ratio test = 16.86, 9 df, P = .051) and neither was a model to predict the implementation of testing (likelihood ratio test = 3.34, 9 df, P = .95). CONCLUSIONS: Less attention is paid to peer review than testing. However, the former is a complementary, and necessary, QA practice rather than an alternative. Therefore, one can argue that there are quality risks, at least at this point in time, in transitioning biomedical OS software into any critical settings that may have operational, financial, or safety implications. Developers of biomedical OS applications should invest more effort in implementing systemic peer review practices throughout the development and maintenance processes.

A Facebook-Based Obesity Prevention Program for Korean American Adolescents: Usability Evaluation.

INTRODUCTION: Adolescent obesity is one of the most serious global public health challenges. Social networking sites are currently popular among adolescents. Therefore, the obesity prevention program for Korean American adolescents was developed on the most popular social networking site, Facebook. The purpose of this study was to evaluate the usability of a culturally tailored Facebook-based obesity prevention program for Korean American adolescents (Healthy Teens). METHOD: An explorative descriptive design of usability testing was used. Usability testing employing one-on-one observation, the think-aloud method, audio taping, screen activity capture, and surveys was performed. Twenty participants were recruited from two Korean language schools (mean age, 15.40 ± 1.50 years). Recruitment and user testing was performed between February and April 2014. Content analysis, using the inductive coding approach, was performed by three coders to analyze transcriptions. Descriptive statistics were used to analyze quantitative data including demographic characteristics, perceived usability, eHealth literacy, and health behaviors. RESULTS: Testing revealed several usability issues in content, appearance, and navigation. Participants' comments regarding content were positive. Although the Facebook platform provided limited flexibility with respect to building the site, participants described the program's appearance as appropriate. Most participants did not experience difficulty in navigating the program. DISCUSSION: Our preliminary findings indicated that participants perceived the Healthy Teens program as usable and useful. This program could be used as a robust platform for the delivery of health education to adolescents. Further research is required to assess the effects of Facebook-based programs on adolescent obesity prevention.

Identifying Home Care Clinicians' Information Needs for Managing Fall Risks.

OBJECTIVES: To help manage the risk of falls in home care, this study aimed to (i) identify home care clinicians' information needs and how they manage missing or inaccurate data, (ii) identify problems that impact effectiveness and efficiency associated with retaining, exchanging, or processing information about fall risks in existing workflows and currently adopted health information technology (IT) solutions, and (iii) offer informatics-based recommendations to improve fall risk management interventions. METHODS: A case study was carried out in a single not-for-profit suburban Medicare-certified home health agency with three branches. Qualitative data were collected over a six month period through observations, semi-structured interviews, and focus groups. The Framework method was used for analysis. Maximum variation sampling was adopted to recruit a diverse sample of clinicians. RESULTS: Overall, the information needs for fall risk management were categorized into physiological, care delivery, educational, social, environmental, and administrative domains. Examples include a brief fall-related patient history, weight-bearing status, medications that affect balance, availability of caregivers at home, and the influence of patients' cultures on fall management interventions. The unavailability and inaccuracy of critical information related to fall risks can delay necessary therapeutic services aimed at reducing patients' risk for falling and thereby jeopardizing their safety. Currently adopted IT solutions did not adequately accommodate data related to fall risk management. CONCLUSION: The results highlight the essential information for fall risk management in home care. Home care workflows and health IT solutions must effectively and efficiently retain, exchange, and process information necessary for fall risk management. Interoperability and integration of the various health IT solutions to make data sharing accessible to all clinicians is critical for fall risk management. Findings from this study can help home health agencies better understand their information needs to manage fall risks.

How Can Home Care Patients and Their Caregivers Better Manage Fall Risks by Leveraging Information Technology?

OBJECTIVES: From the perspectives of home care patients and caregivers, this study aimed to (a) identify the challenges for better fall-risk management during home care episodes and (b) explore the opportunities for them to leverage health information technology (IT) solutions to improve fall-risk management during home care episodes. METHODS: Twelve in-depth semistructured interviews with the patients and caregivers were conducted within a descriptive single case study design in 1 home health agency (HHA) in the mid-Atlantic region of the United States. RESULTS: Patients and caregivers faced challenges to manage fall risks such as unmanaged expectations, deteriorating cognitive abilities, and poor care coordination between the HHA and physician practices. Opportunities to leverage health IT solutions included patient portals, telehealth, and medication reminder apps on smartphones. CONCLUSION: Effectively leveraging health IT could further empower patients and caregivers to reduce fall risks by acquiring the necessary information and following clinical advice and recommendations. The HHAs could improve the quality of care by adopting IT solutions that show more promise of improving the experiences of patients and caregivers in fall-risk management.

Investigating the Challenges and Opportunities in Home Care to Facilitate Effective Information Technology Adoption.

BACKGROUND: As home care utilization increases, information technology (IT) becomes a critical tool for providing quality home care. However, most home health agencies (HHAs) in the United States are in a position to adopt and leverage IT solutions in budget-constrained settings, where it is crucial to address important and pressing challenges and opportunities for achieving effectiveness in IT adoption. OBJECTIVES: (1) Explore HHAs' challenges and opportunities related to delivering home care as well as performing administrative functions and conducting business, (2) learn about current IT implementation levels and activities in home care, and (3) make recommendations to facilitate efforts and initiatives designed for adopting IT in home care effectively. METHODS: Semistructured interviews were conducted to elicit rich contextual information from the participants recruited from 13 local HHAs in one of the states in the United States. Established systems analysis techniques were used to ask questions during the interviews. Framework, a qualitative research method, was used to analyze the qualitative data obtained from the interviews. RESULTS: Coordinating clinical and administrative workflows was an important challenge. Inadequate access to patients' medical history and difficulties with medication reconciliation detracted from the quality of care. Hiring, training, scheduling, and retaining qualified personnel constituted another important challenge. Training and educating patients, caregivers, and families hold important opportunities for improving the quality of care. All except one HHA adopted electronic health records (EHR) but many continued to struggle considerably in their day-to-day functions. Health information exchange (HIE) seems to be the most needed technology. Telehealth solutions were perceived to be promising but their added value and financial viability in the long run were questioned. CONCLUSIONS: The recommendations for effective IT adoption include keeping a quality improvement focus, keeping a holistic organizational perspective, considering potential information exchange problems, addressing education and training needs, experimentation with telehealth if resources permit, considering organization size, and reducing lengthy procedures and excessive documentation requirements. The relevant stakeholders, such as home care professionals, IT vendors, and policy makers, should consider the recommendations from this study to facilitate success in future IT efforts and initiatives in home care.