Toward better public health reporting using existing off the shelf approaches: The value of medical dictionaries in automated cancer detection using plaintext medical data.

OBJECTIVES: Existing approaches to derive decision models from plaintext clinical data frequently depend on medical dictionaries as the sources of potential features. Prior research suggests that decision models developed using non-dictionary based feature sourcing approaches and "off the shelf" tools could predict cancer with performance metrics between 80% and 90%. We sought to compare non-dictionary based models to models built using features derived from medical dictionaries. MATERIALS AND METHODS: We evaluated the detection of cancer cases from free text pathology reports using decision models built with combinations of dictionary or non-dictionary based feature sourcing approaches, 4 feature subset sizes, and 5 classification algorithms. Each decision model was evaluated using the following performance metrics: sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. RESULTS: Decision models parameterized using dictionary and non-dictionary feature sourcing approaches produced performance metrics between 70 and 90%. The source of features and feature subset size had no impact on the performance of a decision model. CONCLUSION: Our study suggests there is little value in leveraging medical dictionaries for extracting features for decision model building. Decision models built using features extracted from the plaintext reports themselves achieve comparable results to those built using medical dictionaries. Overall, this suggests that existing "off the shelf" approaches can be leveraged to perform accurate cancer detection using less complex Named Entity Recognition (NER) based feature extraction, automated feature selection and modeling approaches.

Toward better public health reporting using existing off the shelf approaches: A comparison of alternative cancer detection approaches using plaintext medical data and non-dictionary based feature selection.

OBJECTIVES: Increased adoption of electronic health records has resulted in increased availability of free text clinical data for secondary use. A variety of approaches to obtain actionable information from unstructured free text data exist. These approaches are resource intensive, inherently complex and rely on structured clinical data and dictionary-based approaches. We sought to evaluate the potential to obtain actionable information from free text pathology reports using routinely available tools and approaches that do not depend on dictionary-based approaches. MATERIALS AND METHODS: We obtained pathology reports from a large health information exchange and evaluated the capacity to detect cancer cases from these reports using 3 non-dictionary feature selection approaches, 4 feature subset sizes, and 5 clinical decision models: simple logistic regression, naïve bayes, k-nearest neighbor, random forest, and J48 decision tree. The performance of each decision model was evaluated using sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. RESULTS: Decision models parameterized using automated, informed, and manual feature selection approaches yielded similar results. Furthermore, non-dictionary classification approaches identified cancer cases present in free text reports with evaluation measures approaching and exceeding 80-90% for most metrics. CONCLUSION: Our methods are feasible and practical approaches for extracting substantial information value from free text medical data, and the results suggest that these methods can perform on par, if not better, than existing dictionary-based approaches. Given that public health agencies are often under-resourced and lack the technical capacity for more complex methodologies, these results represent potentially significant value to the public health field.

Enabling Better Interoperability for HealthCare: Lessons in Developing a Standards Based Application Programing Interface for Electronic Medical Record Systems.

We sought to enable better interoperability and easy adoption of healthcare applications by developing a standardized domain independent Application Programming Interface (API) for an Electronic Medical Record (EMR) system. We leveraged the modular architecture of the Open Medical Record System (OpenMRS) to build a Fast Healthcare Interoperability Resources (FHIR) based add-on module that could consume FHIR resources and requests made on OpenMRS. The OpenMRS FHIR module supports a subset of FHIR resources that could be used to interact with clinical data persisted in OpenMRS. We demonstrate the ease of connecting healthcare applications using the FHIR API by integrating a third party Substitutable Medical Apps & Reusable Technology (SMART) application with OpenMRS via FHIR. The OpenMRS FHIR module is an optional component of the OpenMRS platform. The FHIR API significantly reduces the effort required to implement OpenMRS by preventing developers from having to learn or work with a domain specific OpenMRS API. We propose an integration pathway where the domain specific legacy OpenMRS API is gradually retired in favor of the new FHIR API, which would be integrated into the core OpenMRS platform. Our efforts indicate that a domain independent API is a reality for any EMR system. These efforts demonstrate the adoption of an emerging FHIR standard that is seen as a replacement for both Health Level 7 (HL7) Version 2 and Version 3. We propose a gradual integration approach where our FHIR API becomes the preferred method for communicating with the OpenMRS platform.

Examining primary care provider experiences with using a clinical decision support tool for pain management.

Objective: To evaluate primary care provider (PCP) experiences using a clinical decision support (CDS) tool over 16 months following a user-centered design process and implementation. Materials and Methods: We conducted a qualitative evaluation of the Chronic Pain OneSheet (OneSheet), a chronic pain CDS tool. OneSheet provides pain- and opioid-related risks, benefits, and treatment information for patients with chronic pain to PCPs. Using the 5 Rights of CDS framework, we conducted and analyzed semi-structured interviews with 19 PCPs across 2 academic health systems. Results: PCPs stated that OneSheet mostly contained the right information required to treat patients with chronic pain and was correctly located in the electronic health record. PCPs used OneSheet for distinct subgroups of patients with chronic pain, including patients prescribed opioids, with poorly controlled pain, or new to a provider or clinic. PCPs reported variable workflow integration and selective use of certain OneSheet features driven by their preferences and patient population. PCPs recommended broadening OneSheet access to clinical staff and patients for data entry to address clinician time constraints. Discussion: Differences in patient subpopulations and workflow preferences had an outsized effect on CDS tool use even when the CDS contained the right information identified in a user-centered design process. Conclusions: To increase adoption and use, CDS design and implementation processes may benefit from increased tailoring that accommodates variation and dynamics among patients, visits, and providers.

Evaluating a Prototype Clinical Decision Support Tool for Chronic Pain Treatment in Primary Care.

OBJECTIVES: The Chronic Pain Treatment Tracker (Tx Tracker) is a prototype decision support tool to aid primary care clinicians when caring for patients with chronic noncancer pain. This study evaluated clinicians' perceived utility of Tx Tracker in meeting information needs and identifying treatment options, and preferences for visual design. METHODS: We conducted 12 semi-structured interviews with primary care clinicians from four health systems in Indiana. The interviews were conducted in two waves, with prototype and interview guide revisions after the first six interviews. The interviews included exploration of Tx Tracker using a think-aloud approach and a clinical scenario. Clinicians were presented with a patient scenario and asked to use Tx Tracker to make a treatment recommendation. Last, participants answered several evaluation questions. Detailed field notes were collected, coded, and thematically analyzed by four analysts. RESULTS: We identified several themes: the need for clinicians to be presented with a comprehensive patient history, the usefulness of Tx Tracker in patient discussions about treatment planning, potential usefulness of Tx Tracker for patients with high uncertainty or risk, potential usefulness of Tx Tracker in aggregating scattered information, variability in expectations about workflows, skepticism about underlying electronic health record data quality, interest in using Tx Tracker to annotate or update information, interest in using Tx Tracker to translate information to clinical action, desire for interface with visual cues for risks, warnings, or treatment options, and desire for interactive functionality. CONCLUSION: Tools like Tx Tracker, by aggregating key information about past, current, and potential future treatments, may help clinicians collaborate with their patients in choosing the best pain treatments. Still, the use and usefulness of Tx Tracker likely relies on continued improvement of its functionality, accurate and complete underlying data, and tailored integration with varying workflows, care team roles, and user preferences.

Evaluation of electronic recruitment efforts of primary care providers as research subjects during the COVID-19 pandemic.

BACKGROUND: Recruiting healthcare providers as research subjects often rely on in-person recruitment strategies. Little is known about recruiting provider participants via electronic recruitment methods. In this study, conducted during the COVID-19 pandemic, we describe and evaluate a primarily electronic approach to recruiting primary care providers (PCPs) as subjects in a pragmatic randomized controlled trial (RCT) of a decision support intervention. METHODS: We adapted an existing framework for healthcare provider research recruitment, employing an electronic consent form and a mix of brief synchronous video presentations, email, and phone calls to recruit PCPs into the RCT. To evaluate the success of each electronic strategy, we estimated the number of consented PCPs associated with each strategy, the number of days to recruit each PCP and recruitment costs. RESULTS: We recruited 45 of 63 eligible PCPs practicing at ten primary care clinic locations over 55 days. On average, it took 17 business days to recruit a PCP (range 0-48) and required three attempts (range 1-7). Email communication from the clinic leaders led to the most successful recruitments, followed by brief synchronous video presentations at regularly scheduled clinic meetings. We spent approximately $89 per recruited PCP. We faced challenges of low email responsiveness and limited opportunities to forge relationships. CONCLUSION: PCPs can be efficiently recruited at low costs as research subjects using primarily electronic communications, even during a time of high workload and stress. Electronic peer leader outreach and synchronous video presentations may be particularly useful recruitment strategies. TRIAL REGISTRATION: ClinicalTrials.gov , NCT04295135 . Registered 04 March 2020.

Assessing the use of a clinical decision support tool for pain management in primary care.

Objective: Given time constraints, poorly organized information, and complex patients, primary care providers (PCPs) can benefit from clinical decision support (CDS) tools that aggregate and synthesize problem-specific patient information. First, this article describes the design and functionality of a CDS tool for chronic noncancer pain in primary care. Second, we report on the retrospective analysis of real-world usage of the tool in the context of a pragmatic trial. Materials and methods: The tool known as OneSheet was developed using user-centered principles and built in the Epic electronic health record (EHR) of 2 health systems. For each relevant patient, OneSheet presents pertinent information in a single EHR view to assist PCPs in completing guideline-recommended opioid risk mitigation tasks, review previous and current patient treatments, view patient-reported pain, physical function, and pain-related goals. Results: Overall, 69 PCPs accessed OneSheet 2411 times (since November 2020). PCP use of OneSheet varied significantly by provider and was highly skewed (site 1: median accesses per provider: 17 [interquartile range (IQR) 9-32]; site 2: median: 8 [IQR 5-16]). Seven "power users" accounted for 70% of the overall access instances across both sites. OneSheet has been accessed an average of 20 times weekly between the 2 sites. Discussion: Modest OneSheet use was observed relative to the number of eligible patients seen with chronic pain. Conclusions: Organizations implementing CDS tools are likely to see considerable provider-level variation in usage, suggesting that CDS tools may vary in their utility across PCPs, even for the same condition, because of differences in provider and care team workflows.

OpenMRS as a global good: Impact, opportunities, challenges, and lessons learned from fifteen years of implementation.

INTRODUCTION: OpenMRS is an open source medical record system that was first released in 2004. This research study analyzed OpenMRS implementations by conducting a survey of implementers and by reviewing publicly available data reported to the OpenMRS Community to learn about the utilization and impact of OpenMRS over the past 15 years. METHODS: Data about the use of OpenMRS were collected by conducting a survey of OpenMRS implementers that included both quantitative and qualitative questions. Data were also gathered from the OpenMRS community-hosted Atlas website and the OpenMRS Community Annual report to arrive at a comprehensive view of OpenMRS implementations. RESULTS: OpenMRS has been implemented in over 62 countries worldwide (Community Annual report). The survey was responded to by 16 organizations with projects spanning 16 countries, which were launched over 15 years (2004-2019). Fourteen of these sites reported a total of 1,436,357 patients; 4,248,248 visits; 18,028,204 encounters; 312,068,205 observations; and 5088 users, of which 3933 were health providers, recorded in the system database. Implementers reported a positive impact from implementing OpenMRS in streamlining operational processes for healthcare delivery; improved interoperability; improved reporting; improved availability and quality of data for decision making, advocacy, and research; and, improvement in the quality of healthcare delivery. Key challenges in implementing OpenMRS included finding skilled technical staff; acceptability of electronic health records by clinical staff; poor training provided to staff when transitioning from a paper-based to an electronic system; technical challenges, including infrastructure availability (computers, servers, equipment, connectivity, power); missing clinical/programmatic functionality in OpenMRS; poor documentation; and, difficulties faced when contributing code to the open source project. CONCLUSION: OpenMRS has a broad reach globally in a variety of settings. Organizations have reported a positive impact on health care delivery after implementing OpenMRS. Several risks and challenges were identified by implementers that need to be addressed to deliver successful implementations. Continued investment in the development of OpenMRS is needed to sustain and scale its impact.

OpenMRS as an emergency EMR-How we used a global good to create an emergency EMR in a week.

BACKGROUND: As the coronavirus pandemic progressed through the United States, Indianapolis Emergency Medical Services (IEMS) identified a gap between the health system capacity and the projected need to support an overwhelmed health care system. In addressing emergencies or special cases, each medical institution in a metropolitan area typically has a siloed process for capturing emergency patient records. These approaches vary in technical capabilities and may include use of an electronic medical record system (EMR) or a hybrid paper/EMR process. Given the projected volume of patients for the COVID-19 pandemic and the proposed multi-institutional team approach needed in case of significant provider illness, IEMS sought a simple, efficient, consolidated EMR solution to support planning for the potential capacity gap. IEMS approached Regenstrief Institute (RI), an established partner with experience in supporting OpenMRS, a global good EMR platform that had been deployed in multiple settings globally. OBJECTIVE: The purpose of this project was to determine if OpenMRS, a global good, could be used to quickly stand up a system that would meet the needs for health emergency data collection and reporting. DESIGN AND IMPLEMENTATION METHODS: The team used an "all hands on deck" approach, bringing together technical and subject matter experts, and a human-centered and iterative process to ensure the system met the key needs of IEMS. The OpenMRS Reference Application was adapted to the specific need and deployed as Docker containers to servers within the Indiana Health Information Exchange. PROJECT OUTCOMES AND LESSONS LEARNED: In less than two weeks, the Regenstrief team was able to install, configure and set up a working version of OpenMRS to support the desired electronic record requirements for the IEMS disaster field clinics. Using a human-centered approach, the RI team developed, tested, and released a user-friendly, installation-ready solution complete with an end user manual and a base support plan. IEMS and RI are sharing this approach to demonstrate how a global good can quickly generate a solution for COVID-19 and other disaster responses. CONCLUSIONS: Open source global goods can rapidly be adapted to meet local needs in an emergency. OpenMRS can be adapted to meet the needs of basic emergency medical services registration, triage, and basic data collection.

Regenstrief teaching electronic medical record (tEMR) platform: a novel tool for teaching and evaluating applied health information technology.

The objective of this study is to provide an overview of the Regenstrief Teaching Electronic Medical Record (tEMR), how the tEMR could be used, and how it is currently being used in health professions education. The tEMR is a derivative of a real-world electronic health record (EHR), a large, pseudonymized patient database, and a population health tool designed to support curricular goals. The tEMR has been successfully adopted at 12 health professional, public health, and health information technology (HIT) schools, with over 11 800 unique student users and more than 74 000 logins, for case presentation, to develop diagnostic and therapeutic plans, and to practice documentation skills. With the exponential growth of health-related data and the impact of HIT on work-life balance, it is critical for students to get early EHR skills practice and understand how EHR's work. The tEMR is a promising, scalable, flexible application to help health professional students learn about common HIT tools and issues.

Determinants of Long Immunization Clinic Wait Times in a Sub-Saharan African Country.

The wait time clients spend during immunization clinic visits in low- and middle-income countries is a not well-understood reported barrier to vaccine completion. We used a prospective, observational design to document the total time from client arrival-to-discharge and all sequential provider-client activities in 1 urban, semi-urban, and rural immunization clinic in Nigeria. We also conducted caregiver and provider focus group discussions to identify perceived determinants of long clinic wait times. Our findings show that the time from arrival-to-discharge varied significantly by the clinic and ranged between 57 and 235 minutes, as did arrival-to-all providers-client activities. Focus group data attributed workflow delays to clinic staff waiting for a critical mass of clients to arrive for their immunization appointment before starting the essential health education talk or opening specific vaccine vials. Additionally, respondents indicated that complex documentation processes caused system delays. Research on clinic workflow transformation and simplification of immunization documentation is needed.

Leapfrogging paper-based records using handheld technology: experience from Western Kenya.

PROBLEM: There is limited experience with broad-based use of handheld technologies for clinical care during home visits in sub-Saharan Africa. OBJECTIVE: We describe the design, development, implementation, and evaluation of a PDA/GPS-based system currently used during home visits in Western Kenya. RESULTS: The system, built on Pendragon Forms, was used to create electronic health records for over 40,000 individuals over a three-month period. Of these, 1900 represented cases where the individual had never received care for the identified condition in an established care facility. On a five-point scale, and compared to paper-and-pen systems, end-users felt that the handheld system was faster (4.4±0.9), easier to use (4.5±0.8), and produced higher quality data (4.7±0.7). Projected over three years to cover two million people, use of the handheld technologies would cost about $0.15 per person--compared to $0.21 per individual encounter entered manually into a computer from a paper form. CONCLUSION: A PDA/GPS system has been successfully and broadly implemented to support clinical care during home-based visits in a resource-limited setting.

Experience implementing electronic health records in three East African countries.

INTRODUCTION: Efficient use of health care resources in low-income countries by providers and local and national managers requires timely access to patient data. OBJECTIVE: To implement electronic health records (EHRs) in HIV clinics in Kenya, Tanzania, and Uganda. RESULTS: We initially developed and implemented an EHR in Kenya through a mature academic partnership. The EHR was then implemented in six HIV clinics in Tanzania and Uganda in collaboration with their National AIDS Control Programmes. All implementations were successful, but the system's use and sustainability varied depending on who controlled clinic funding. CONCLUSIONS: Successful EHR use and sustainability were enhanced by local control of funds, academic partnerships (mainly by leveraging research funds), and in-country technology support.

Factors That Influence Changes to Existing Chronic Pain Management Plans.

BACKGROUND: The objective of this qualitative study is to better understand primary care clinician decision making for managing chronic pain. Specifically, we focus on the factors that influence changes to existing chronic pain management plans. Limitations in guidelines and training leave clinicians to use their own judgment and experience in managing the complexities associated with treating patients with chronic pain. This study provides insight into those judgments based on clinicians' first-person accounts. Insights gleaned from this study could inspire innovations aimed at supporting primary care clinicians (PCCs) in managing chronic pain. METHODS: We conducted 89 interviews with PCCs to obtain their first-person perspective of the factors that influenced changes in treatment plans for their patients. Interview transcripts were analyzed thematically by a multidisciplinary team of clinicians, cognitive scientists, and public health researchers. RESULTS: Seven themes emerged through our analysis of factors that influenced a change in chronic pain management: 1) change in patient condition; 2) outcomes related to treatment; 3) nonadherent patient behavior; 4) insurance constraints; 5) change in guidelines, laws, or policies; 6) approaches to new patients; and 7) specialist recommendations. CONCLUSIONS: Our analysis sheds light on the factors that lead PCCs to change treatment plans for patients with chronic pain. An understanding of these factors can inform the types of innovations needed to support PCCs in providing chronic pain care. We highlight key insights from our analysis and offer ideas for potential practice innovations.

Decision-Centered Design of Patient Information Visualizations to Support Chronic Pain Care.

BACKGROUND: For complex patients with chronic conditions, electronic health records (EHRs) contain large amounts of relevant historical patient data. To use this information effectively, clinicians may benefit from visual information displays that organize and help them make sense of information on past and current treatments, outcomes, and new treatment options. Unfortunately, few clinical decision support tools are designed to support clinical sensemaking. OBJECTIVE: The objective of this study was to describe a decision-centered design process, and resultant interactive patient information displays, to support key clinical decision requirements in chronic noncancer pain care. METHODS: To identify key clinical decision requirements, we conducted critical decision method interviews with 10 adult primary care clinicians. Next, to identify key information needs and decision support design seeds, we conducted a half-day multidisciplinary design workshop. Finally, we designed an interactive prototype to support the key clinical decision requirements and information needs uncovered during the previous research activities. RESULTS: The resulting Chronic Pain Treatment Tracker prototype summarizes the current treatment plan, past treatment history, potential future treatments, and treatment options to be cautious about. Clinicians can access additional details about each treatment, current or past, through modal views. Additional decision support for potential future treatments and treatments to be cautious about is also provided through modal views. CONCLUSION: This study designed the Chronic Pain Treatment Tracker, a novel approach to decision support that presents clinicians with the information they need in a structure that promotes quick uptake, understanding, and action.

Information Needs and Requirements for Decision Support in Primary Care: An Analysis of Chronic Pain Care.

Decision support system designs often do not align with the information environments in which clinicians work. These work environments may increase Clinicians' cognitive workload and harm their decision making. The objective of this study was to identify information needs and decision support requirements for assessing, diagnosing, and treating chronic noncancer pain in primary care. We conducted a qualitative study involving 30 interviews with 10 primary care clinicians and a subsequent multidisciplinary systems design workshop. Our analysis identified four key decision requirements, eight clinical information needs, and four decision support design seeds. Our findings indicate that clinicians caring for chronic pain need decision support that aggregates many disparate information elements and helps them navigate and contextualize that information. By attending to the needs identified in this study, decision support designers may improve Clinicians' efficiency, reduce mental workload, and positively affect patient care quality and outcomes.

Experience in implementing the OpenMRS medical record system to support HIV treatment in Rwanda.

The challenge of scaling up HIV treatment in Africa has led to a new emphasis on improving health systems in impoverished areas. One aspect of this is the development and deployment of electronic medical record systems to support HIV and TB treatment. In this paper we describe the design and implementation of a new medical record architecture to support an HIV treatment program in rural Rwanda. The architecture is called OpenMRS and it has been developed to address the problem of configuring EMR systems to suit new sites, languages and diseases. OpenMRS uses a data dictionary called the concept dictionary to represent all the possible data items that can be collected. This allows new items to be added to the system by non-programmers. In addition, there are form creation tools that use drag and drop web technologies to simplify form construction. The OpenMRS system was first implemented in Kenya in February 2006 and then in Rwanda in August 2006. The system is now functioning well and we are developing extensions to improve the support for the clinic. These include improved, easy to use reporting tools, support for additional clinical problems including nutrition and child health, better database synchronization tools, and modules to collect laboratory data and support the pharmacy. The system is also in use in South Africa and Lesotho and is being deployed in Tanzania and Uganda.

The AMPATH medical record system: creating, implementing, and sustaining an electronic medical record system to support HIV/AIDS care in western Kenya.

Providing high-quality HIV/AIDS care requires high-quality, accessible data on individual patients and visits. These data can also drive strategic decision-making by health systems, national programs, and funding agencies. One major obstacle to HIV/AIDS care in developing countries is lack of electronic medical record systems (EMRs) to collect, manage, and report clinical data. In 2001, we implemented a simple primary care EMR at a rural health centre in western Kenya. This EMR evolved into a comprehensive, scalable system serving 19 urban and rural health centres. To date, the AMPATH Medical Record System contains 10 million observations from 400,000 visit records on 45,000 patients. Critical components include paper encounter forms for adults and children, technicians entering/managing data, and modules for patient registration, scheduling, encounters, clinical observations, setting user privileges, and a concept dictionary. Key outputs include patient summaries, care reminders, and reports for program management, operating ancillary services (e.g., tracing patients who fail to return for appointments), strategic planning (e.g., hiring health care providers and staff), reports to national AIDS programs and funding agencies, and research.

Leveraging the Value of Human Relationships to Improve Health Outcomes. Lessons learned from the OpenMRS Electronic Health Record System.

OBJECTIVES: Despite significant awareness on the value of leveraging patient relationships across the healthcare continuum, there is no research on the potential of using Electronic Health Record (EHR) systems to store structured patient relationship data, or its impact on enabling better healthcare. We sought to identify which EHR systems supported effective patient relationship data collection, and for systems that do, what types of relationship data is collected, how this data is used, and the perceived value of doing so. MATERIALS AND METHODS: We performed a literature search to identify EHR systems that supported patient relationship data collection. Based on our results, we defined attributes of an effective patient relationship model. The Open Medical Record System (OpenMRS), an open source medical record platform for underserved settings met our eligibility criteria for effective patient relationship collection. We performed a survey to understand how the OpenMRS patient relationship model was used, and how it brought value to implementers. RESULTS: The OpenMRS patient relationship model has won widespread adoption across many implementations and is perceived to be valuable in enabling better health care delivery. Patient relationship information is widely used for community health programs and enabling chronic care. Additionally, many OpenMRS implementers were using this feature to collect custom relationship types for implementation specific needs. CONCLUSIONS: We believe that flexible patient relationship data collection is critical for better healthcare, and can inform community care and chronic care initiatives across the world. Additionally, patient relationship data could also be leveraged for many other initiatives such as patient centric care and in the field of precision medicine.

Overcoming the Maternal Care Crisis: How Can Lessons Learnt in Global Health Informatics Address US Maternal Health Outcomes?

Despite unprecedented spending, US maternal outcomes have worsened drastically over the past decade. In comparison, maternal outcomes of many Low and Middle-Income Countries (LMIC) have improved. Lessons learnt by their success may be applicable to the US. We performed a literature review to identify innovations that had met with success across LMIC, and should be considered for adoption in the US. mHealth and patient facing alerts, Telehealth, patient controlled health records, inclusion of patient relationship data in health information systems and positioning empowered community health workers as catalysts of maternal care delivery were identified as innovations worthy of further evaluation. These innovations were categorized into several themes; knowledge, technology, patient/community empowerment, coordination and process change. Tools that place informed and empowered patients and community members at the center of maternal care has greatly improved maternal outcomes, and are suitable to be considered for the US healthcare system.