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The Johns Hopkins ACG System is the world’s leading population health analytics software. The system continues to evolve, providing ever-more refined tools used in the US and across the glove for over 30 years, from commercial health plans and governments to health systems and large employers. The beauty of the ACG System is its ability to combine data from an array of sources to reveal powerful insights that go beyond just medical records. By identifying risk and tracking patients over time, the ACG System can help you plan ahead and reduce health care costs – especially valuable to risk-bearing health systems and provider organizations. Most importantly, the ACG System allows you to be proactive rather than reactive when it comes to your population’s unique health care needs. The system helps you combine a population-level perspective with patient-level behaviors and conditions. And because the system is incredibly flexible and responsive to new information, you can rest assured that no matter what comes next, the ACG System will continuously adapt to your health care management needs.
The Johns Hopkins ACG System offers a unique approach to measuring morbidity that improves accuracy and fairness in evaluating clinician performance, identifying patients at high risk, forecasting health care utilization and setting equitable payment rates.
What is an “ACG®” or “Adjusted Clinical Group®“?
Based on the premise that clustering of morbidity is a better predictor of health care services resource use than the presence of specific diseases or disease hierarchies, the ACG System provides a multi-morbidity framework that is clinically logical, informative of future health care resources, easy to use and applicable to both financial and clinical managers.
The Johns Hopkins ACG® System was developed and continues to be validated and improved by experts at the Johns Hopkins Bloomberg School of Public Health (Center for Population Health IT). The diverse ACG System team is comprised of Johns Hopkins faculty and staff including clinicians, health services researchers and Health Information Technology experts. Johns Hopkins HealthCare Solutions is comprised of experts in population health as well as business, marketing and product development. Together, we develop, maintain and offer the ACG System to commercial health plans, governments, health systems, large employers and researchers worldwide.
The following third-party products enhance and add specific functionality (such as report generation) to the ACG System software, strengthening the end-user’s experience and results. Click on any of the below images to view detailed information about those products.
For questions about extending the ACG System functionality to better fit your specific needs, or if you have an Extension you would like featured on this page, please contact your account manager, or fill out the contact form.
Inclusion of the above resources does not imply an endorsement of the companies or the products/services they provide. See Terms page for details.
The ACG System provides models based on medical data only, pharmacy data only or combined medical and pharmacy data. You can choose the most appropriate model from a suite of predictive models based upon your available data and intended purpose. Predictions of both total cost and pharmacy cost are available. We also offer models that predict hospitalization.
To see the ACG System in action and speak to a Team member about your specific needs, request a demo today.
The ACG System is the only population-based system, which is totally adaptable to a local context. While our beginnings come from the U.S. health care system, we have proven success in using the ACG System internationally, taking into consideration local cost structures, coding systems, practice behavior, and language adaptations as well as the availability of local markers whether they capture socio-economic data, functionality, living arrangements, or other measures.
These new variables can be incorporated into user-created Risk Assessment Variable Sets (RAVS) that can be substituted for the supplied default case mix weights and predictive models. The ACG System supports a wide range of diagnostic and pharmacy code sets, including ICPC, ICD9, ICD9-CM, ICD10, ICD10-CM, SNOMED CT, NDC, ATC, and Read Codes.
To see the ACG System in action and speak to a Team member about your specific needs, request a demo today.
The ACG System’s “person-centered” approach allows you to capture the multidimensional nature of an individual’s health over time. Further, it reflects the entire experience a patient has within a health care system, not just hospital visits as other case mix methodologies do.
This has the benefit of capturing the entirety of morbidity and health care that a person experiences over time and across service settings. Knowing the totality of a patient’s health care needs allows you to effectively anticipate resource consumption.
Because categorization is based on patient needs, as reflected in clinical diagnoses rather than in a clinician’s practice pattern, the ACG System is not prone to the potential shortcomings of other systems that can categorize patients into “sicker” categories because a clinician’s practice style is more intensive.
Moreover, the ACG System does not provide perverse incentives for clinicians to perform unnecessary procedures or hospitalize patients to increase their risk rating and thereby secure higher levels of compensation.
To see the ACG System in action and speak to a Team member about your specific needs, request a demo today.
The ACG System describes and manages healthy populations (i.e., low users of health care services) as well as sicker populations. Unlike other systems, the ACG System was developed using commercial managed care populations as well as U.S. state Medicaid populations, both of which closely resemble the general population.
Most other case mix risk adjustment methods are developed using sicker, more specialized patient populations such as Medicare, or disabled populations and have only later been adapted or modified to suit the needs of a more general patient population.
The ACG System provides more than a simple statistically-derived score; the ACG System offers a complete taxonomy to describe population health.
To see the ACG System in action and speak to a Team member about your specific needs, request a demo today.
The ACG System describes the health status of all persons in a population, ranging from those with relatively minor medical needs, such as preventive services and acute infections, to the most expensive, sickest 5% of the population that consumes 50% of all health care resources.
This characteristic of the ACG System makes it well-suited for several applications, including identifying high-risk patients with multiple morbidities, describing the morbidity burden of a population, assessing primary care provider performance, profiling entire health systems, targeting groups of patients who would benefit from care management, and rate-setting.
To see the ACG System in action and speak to a Team member about your specific needs, request a demo today.
The ACG System team regularly improves and develops new iterations of the software. We are pleased to share highlights of the newly released Johns Hopkins ACG System Version 11.1. The focus of which is to continue to improve not only the predictive accuracy for identifying high-risk members, but also to provide robust clinical categorization to assess and stratify populations.
ACG System Version 11.1 release highlights include:
For additional information on the ACG System Version 11.1 release including details, presentations, previews and more please contact your primary support person or email info@HopkinsACG.org.
Chad Abrams, Director of U.S. Operations, ACG System; briefly describes the Johns Hopkins ACG System, and highlights features included in the software’s Version 11 release.
To see the ACG System in action and speak to a Team member about your specific needs, request a demo today.
Kumar is the Executive Director for Population Health Analytics at Johns Hopkins HealthCare Solutions. He is responsible for the day-to-day management of the business line and for the development and execution of its long-term domestic and international commercial strategy, centered around the Johns Hopkins ACG System, which transforms healthcare data into actionable information.
Kumar has a strong track record as an innovator in the development and commercialization of health care information technologies. Most recently, he was the chief information officer (CIO) of ShareCare Healthways’ Population Health division, where he enhanced their technology infrastructure and product portfolio and reimagined and built a new program delivery platform that reduced operating expenses. Prior to that, Kumar was the CIO of Humana Inc.’s Behavioral Health division, where his work in systems and business integration, joint venture enablement, portfolio modernization, and process optimization, earned him the company’s President’s award.
Kumar recently completed his Doctorate in Business Administration at the Walden University School of Technology and Business. He has an M.B.A. in entrepreneurship from the University of Louisville, KY, a Bachelor of Engineering from Pune University, and has also completed a Chartered Financial Analyst program.
Dr. Sutch has been working with the ACG System Team since 2007. He has over 20 years experience of working in the National Health Service (NHS) in Great Britain at local, regional and national levels. He has worked with data and data collections from various aspects of activity and financial returns. His lead role in the design of HRGv35, a tool currently used for hospital payments in NHS England– and its updated version HRGv4– led to the development of analytical methods on routinely collected NHS data as well as other clinical data collections. He has an extensive working and practical knowledge of the HES/NWCS/SUS data collections.
He also worked on benchmarking systems for commissioning in Great Britain to develop a national system of tools, first in NHS Wales and later in England. As the Analytical and Statistical Services Manager of the NHS Information Authority National, Dr. Sutch oversaw the continued development of national benchmarking products, and supported the initial development of the program budget categories. He also sat on the committees responsible for the implementation of ICD-10 codes in England and Wales, and then led the development of HRGs that formed a major contribution to the redevelopment of the surgical classification (OPCS4.3, 4.4).
Through the Kings Fund project, and subsequent projects with various PCTs, Dr. Sutch has gained expertise in the application of ACGs® to NHS ambulatory data.
Mr. Thompson joined the ACG System Team in 2013. As Director of User Support, Alan’s primary responsibilities include supporting users, collaborators, and team members in maximizing the use of the ACG System. He also coordinates the UK Team in facilitating applications of the ACG System in the UK NHS.
A registered nurse by background, Mr. Thompson has over 30 years’ experience in the UK National Health Service (NHS) in clinical, operational and project management posts within hospitals and primary care. He has a keen interest in the use of information to support managerial and clinical decision-making and to inform improvements in the provision of patient-centered services.
Between 2010 and 2013, Mr. Thompson led a project to implement the ACG System across 600 GP Practices in the south of England. This role supported clinicians in optimizing the use of risk stratification information to inform clinical programs such as case management and integrated care teams. He also worked with Clinical Commissioning Groups (CCGs) to use risk stratification to support commissioning activities.
As a firm believer in sharing experience and expertise across the NHS, he established both the Southern England and national UK ACG System User groups to facilitate conversations and shared learning among NHS users of the ACG System.
James Barrett, MA, MB, BChir, MSc; Senior Analyst, UKDr. Barrett has been with the ACG System Team since 2009. He provides technical support to ACG System users within the NHS. He worked for 20 years in health care informatics and has a particular interest in OLAP and web-based analytical tools. He has medical qualifications and is interested in all aspects of the application of information technology in health care. He was a member of the SNOMED CT Technical Design team and has extensive experience working with the Read Code authoring project. Joining the NHS Information Authority in 1999, he worked on reporting tools to provide interactive access to HES data for NHS users. More recently he has worked for a number of years with PRIMIS+ analyzing data quality in primary care, and for the National Diabetes Audit at the NHS Information Centre analyzing the care given to patients with diabetes.
Dr. Barrett received his MA from Cambridge University.
Dr. Lemke is a senior member of the ACG System R&D team at the Center for Population Health IT (CPHIT) in the Department of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health. He actively contributes to the process of moving new tools for health care administration from software specification to testing and validation. He has also been a frequent presenter at ACG User conferences and has co-authored multiple academic papers that have featured ACG System components.
Dr. Lemke joined Johns Hopkins University in 1998. His prior work experience was as a statistician with the Maryland Department of Health and Mental Hygiene and the Maryland Healthcare Commission. He has vast experience in claims processing, risk adjustment, predictive modeling, biostatistics, epidemiology, and statistical programming and data visualization. He successfully applied the ACG System in pilot projects to National Health Insurance schemes in the United Kingdom and Germany, which has contributed to the adoption of the ACG System in several European countries. He has also participated in the development of key functionality into the ACG System, including the Expanded Diagnosis Clusters, and developed hospitalization and readmission prediction models, condition indicators based on laboratory results, and an emergency department visit classification algorithm. He continues to work on multiple ACG System projects with electronic medical records and claims.
Dr. Lemke received his Doctor of Philosophy degree in Statistics and Industrial Engineering from Iowa State University after he completed a master’s program in Statistics at the University of Georgia.
Nihat Yurt, MD; Senior Consultant, EurasiaDr. Yurt worked for the Ministry of Health in Turkey between 2000 and 2009, as the coordinator of the eHealth efforts within the eTransformation Turkey Project. Dr. Yurt was appointed by the Prime Ministry as the Deputy Head of e-Government Advisory Group in 2009. He directed the e-Prime Ministry Project and was involved in drafting e-legislation and international activities on e-health and e-government. He has taken an active role in eEurope+, eTurkey and eTransformation Turkey projects and in the development of the Action Plans for the eTransformation Turkeyproject and Information Society Strategy carried out by the State Planning Organization. He has broad knowledge, experience and leadership on the strategy and policy of eGovernment, eHealth and ICT technologies.
In June, 2010, Dr. Yurt took on the role of Senior Consultant for the ACG System Team, focusing on the Middle East and Gulf States, as well as assisting with various EU eHealth initiatives.
Since 2010, he has been working for the Public Administration Institute for Turkey and Middle East (TODAIE) as the Institute’s physician and also has been providing support to e-Government Center activities and projects. Dr. Yurt graduated with a Medical Doctor degree from the Ankara University Faculty of Medicine in 1998.
Ms. Martyn joined the ACG System Team in 2014. Her focus is on providing strategic support to grow the ACG System, both with existing and prospective users. Her French and Spanish speaking skills are particularly useful for regions where those languages are spoken.
Prior to joining the ACG System Team, Ms. Martyn worked for 6 years in a variety of positions in local government in the UK. She also had the opportunity to manage the implementation of health care reforms with the NHS at the local and regional level in London. Most recently, she worked with Johns Hopkins Medicine International on the Latin America and Caribbean team.
Ms. Martyn received her MSPH in Health Policy from The Johns Hopkins Bloomberg School of Public Health. She also holds a postgraduate diploma from Warwick Business School.
Walter Fenner has more than 20 years of international experience in strategic business development, planning, budgeting, operations, project management, and marketing. As part of the ACG System team, Mr. Fenner manages the back-office operations, working closely with the ACG System development and licensing teams as well as more than two dozen international customers.
Before joining Solutions, Mr. Fenner worked in a range of management positions at the Johns Hopkins University and in the public and private sector in both the United States and Europe.
Mr. Fenner earned a Master of Business Administration degree from European University, Switzerland and a bachelor’s degree from the University of the Nations, USA.
Giulianna Peri brings to the team more than 20 years of leadership experience as a marketing, operations, and information technology executive in health care, telecommunications, and architecture and planning.
Throughout her career, Ms. Peri has worked with leading academic medical centers and corporate organizations to advance their business objectives by creating and managing e-commerce initiatives, developing new business processes, improving marketing operations, and facilitating partnerships worldwide.
Prior to joining the Johns Hopkins team, Ms. Peri lead an organization pioneering the online commercialization of intellectual property between the top health systems in the United States and around the world.
A native Spanish speaker, Ms. Peri earned a Master of Science degree in management of information systems and e-commerce from Nova Southeastern University and a Bachelor of Science degree in international business and French from Florida Atlantic University.
Sollis Clarity Health Analytics
Sollis Clarity Health Analytics, with the integrated ACG System, is health care analytics software that helps National Health Service (NHS) commissioners and clinicians understand patterns of multi-morbidity and its relationship to utilization, costs and outcomes.
Sollis Clarity Health Analytics goes beyond patient level risk stratification. Risk stratification at a population level helps the health economy — providers and commissioners — analyze and minimize the progression of diseases and the exacerbation of co-morbidities. When combined with the ACG System it is a comprehensive family of measurement tools designed to help explain and predict how health care resources are delivered and consumed.
The following video describes how using risk adjustment and case mix analysis can help commissioners evaluate health care expenditure across providers and set more informed and equitable budgets or capitation rates.
Analyses and insights into the population profile of a Sustainability and Transformation Partnership
Sollis was commissioned by a Sustainability and Transformation Partnership (STP) to provide analyses to help them better understand their population profile.Using the population health management tools available in Sollis Clarity Health Analytics and the Johns Hopkins University Adjusted Clinical Groups® (ACG® System), our analyses revealed some fascinating insights, which you can read about in the PDF available here.
Visit the Sollis website or their social media channels
Inclusion of the above resources does not imply an endorsement of the companies or the products/services they provide. See Terms page for details.
“Person-focused” health care management means genuinely knowing your members and accurately forecasting their needs over time. Identifying patients at high risk, understanding provider performance, forecasting health care utilization based on medical and/or pharmacy claims, and identifying equitable payment rates can help you measure morbidity and help improve accuracy and fairness.
The Johns Hopkins ACG System, included in the CareAnalyzer® solution has been used in commercial and research settings worldwide, longer and more extensively than any other system on the market today. It uniquely helps payers measure the impact of multi-morbidity by recognizing patterns of disease. As the exclusive U.S. commercial distributor of the Johns Hopkins ACG System, DST helps payers in all markets.
Click for a detailed overview of DST CareAnalyzer
For more information on DST please visit the DST Website
Inclusion of the above resources does not imply an endorsement of the companies or the products/services they provide. See Terms page for details.
Tableau® is a powerful graphical reporting tool used by many ACG System analysts to allow their supervisors to easily generate their own reports.
The following example comes from an ACG System licensee using Tableau Public, a free web-based reporting tool:
For more information on Tableau please visit their website
Inclusion of the above resources does not imply an endorsement of the companies or the products/services they provide. See Terms page for details.
Qlik® is a powerful graphical reporting tool used by many ACG System analysts to allow their supervisors to easily generate their own reports. The following example comes from an ACG System licensee in the Veneto Region of Italy who uses Qlik® to display ACG System data output, and export information into a report that is easily shared and understood:
Click for an overview describing the integration of the ACG System with the business intelligence system of a Veneto Region health unit.
For more information on Qlik please visit their website.
Inclusion of the above resources does not imply an endorsement of the companies or the products/services they provide. See Terms page for details.
See what is driving costs, determine which programs to implement, and measure the results of those changes. Their clear, organized reports tell your story like you have never seen it told before.
After performing a careful, thorough analysis of your claims, they can provide actionable feedback on plan modifications that will improve member health and lower costs.
Low cost providers are one thing. Effective providers with superior results are another. You want to see outcome-adjusted costs. They help you identify which providers deliver the best total outcomes.
Their predictive analytics identify more prospective high cost claimants with great accuracy, allowing you to direct your scarce healthcare resources efficiently and achieve the highest return on investment.
For more information on Data Smart please visit their website.
Inclusion of the above resources does not imply an endorsement of the companies or the products/services they provide. See Terms page for details.
The ACG System can be integrated into Epic’s Healthy Planet:
For more information on Epic please visit their website.
eClinicalWorks is a cloud-based EHR used by thousands of doctors, nurse practitioners and medical professionals.
The Cloud keeps your patient and practice data safe, secure, and fully accessible regardless of time, network traffic, or Mother Nature. With 9 data centers across the country, data travels on the shortest path possible — increasing reliability, scalability, greater uptime, and consistent high-speed access.
The eClinicalWorks Cloud keeps your private data private. Concerned about performance or upgrades? Since you’re on your own database, your system performance will never be affected by another practice’s upgrades or large search queries. You can also schedule upgrades when they’re convenient for you.
For more information on eClinicalWorks please visit their website
Inclusion of the above resources does not imply an endorsement of the companies or the products/services they provide. See Terms page for details.
Now integrated into the Johns Hopkins ACG System, Medscheme’s Emerging Risk Model is the result of combining innovative predictive modeling research with Medscheme’s proven track record of designing and implementing successful care management programs.
Based on the Emerging Risk model, Medscheme has implemented a program aimed at intervening on individuals within these lower morbidity groups who are most likely to incur increasing health care costs in the future, but who are still amenable to care management intervention programs.
What differentiates this approach from other methods of identifying patients for care management programs is the recognition that when tracking costs over future time periods for individuals in the lower Resource Utilization Bands (RUBs), the average health care expenditure for those individuals increases dramatically despite their current costs being relatively lower than those associated with higher RUBs. Using the ACG System, a predictive model was developed to predict the likelihood of individuals moving to a higher RUB in the future.
The figure below illustrates the more proactive approach enabled by the Emerging Risk Model by highlighting the difference in disease burden between individuals identified as high risk and individuals identified as emerging risk at the point of identification for clinical intervention. In addition, the rapid increase in disease burden of those identified as emerging risk in the absence of clinical intervention is evident. By identifying these individuals early their progress to eventual high risk status can be prevented.
The potential impact of this model resulted in Medscheme receiving the inaugural Starfield Award for best use of the Johns Hopkins ACG System in creating a comprehensive care management program focused on both emerging and high risk individuals.
Now available to global health care markets, the Emerging Risk Model allows users to address the changing health care needs of a greater proportion of the population at a far earlier stage and in a far more systematic and sustainable way.
Click for a detailed overview of the Medscheme Emerging Risk Model
For more information on Medscheme:
Visit the Medscheme website
Contact Cristina Mannie
Contact Stefan Strydom
In 2013 HealthPartners received an endorsement from the National Quality Forum (NQF) for their Total Cost of Care and Resource Use Measures (TCOC) methodology, which incorporates the ACG System.
HealthPartners’ Total Cost of Care and Resource Use Measures (TCOC) framework addresses one of the most fundamental problems related to population health: rising health care costs. TCOC is designed to support affordability initiatives, to identify instances of overuse and inefficiency, and to highlight cost-saving opportunities. It is a full-population, person-centered measurement tool that accounts for 100% of the care provided to a patient. Additionally, it supplies a reporting suite to support multiple levels of analysis.
More information about the TCOC methodology can be found on the HealthPartners website. For information about, and access to the TCOC Toolkit click here.
Click here to read the NRHI Report: From Claims to Clarity: Deriving Actionable HealthcareCost Benchmarks from Aggregated Commercial Claims Data
The ACG System is a key component of the TCOC methodology. If you are not yet an ACG System user, please click here for information on acquiring a license.
Dr. Weiner is a professor of health policy and management at the Johns Hopkins Bloomberg School of Public Health. He is also a professor of health sciences informatics at the Johns Hopkins School of Medicine, and currently co-director of the Johns Hopkins Center for Population Health IT (CPHIT). He is the director of the Public Health Informatics training program at Johns Hopkins. His current research focuses on the integration and application of Big Data from the medical, insurance, public health and social service sectors to population-based analytics and interventions within communities and delivery systems.
During his more than 35 years with Johns Hopkins, Dr. Weiner served as the director of the health services research and policy PhD program and as the deputy director of the Health Services Research and Development Center. He is the founder of the Johns Hopkins Center for Population Health IT, an innovative cross-disciplinary center focusing on the application of EHR, e-health and m-health data sources to public health, population health and health care system improvement. Lastly, Dr. Weiner is the co-developer and Scientific Director of the Johns Hopkins ACG System.
Dr. Weiner received a bachelor’s degree in Human Biology from the University of Pennsylvania. He received a master’s degree in Health Administration from the University of Massachusetts-Amherst, and a Doctorate of Public Health in Health Services Research and Policy from Johns Hopkins.
Dr. Bodycombe is responsible for formulating and overseeing the R&D agenda of the lab. He is actively engaged in all phases of moving the work of the lab from initial conceptualization, methodological design, and analytics, to deployment and publication. He has more than 40 years of experience in statistics, the design and construction of large clinical and epidemiologic databases, statistical programming, and information technology applications development.
Prior to joining the Johns Hopkins Bloomberg School of Public Health faculty, Dr. Bodycombe served as Vice President of Analytics for Premier Inc., a major quality improvement and supply chain management organization. In addition, he was Director of Research for the American College of Cardiology. He has also consulted on a wide range of projects concerning prison healthcare, Native American sanitation programs, and HIV in minority communities.
Dr. Bodycombe holds a ScD in Health Policy and Management and a Baccalaureate degree in Biology from the Johns Hopkins University, as well as an MSc in Biostatistics from Georgetown University.
Hong Kan, PhD, MA, MPP is an associate scientist in the Department of Health Policy and Management at Johns Hopkins Bloomberg School of Public Health and a core faculty member at CPHIT. He has a background in health services research, statistics, and machine learning. Before joining Johns Hopkins, he had over a decade of outcomes research experience in the managed care and pharmaceutical industries. His primary research interests lie in outcomes research, health economics, risk adjustment, and related methodologies including predictive analytics and causal inference. He brings academic research and methodological expertise along with broad industry research experience and real-world perspectives.
Mr. Richards has been with the ACG System Team since 1989. An electrical engineer by training, Mr. Richards designed and programmed all command line versions of the ACG System software, including Windows, all UNIX variants, and the assembly language MVS version. Mr. Richards provides technical support to the ACG System Team on the development of data bases used for further research and development of the ACG System products. Mr. Richards also created SKYTALE, HIPAA compliant software for encrypting/de-encrypting patient identifiers on claim level data. He was the chief technical architect for the Sentinel Centers Network database and has spoken at conferences on the use of ACGs with data from federally funded community health centers.
Dr. Chang joined the ACG System team in 2003. He applied the ACG System in Taiwan’s National Health Insurance scheme showing that the ACG System could be adopted in Asian countries, despite the different health care systems from those in western countries. He has also participated in the incorporation of the longitudinal element into the ACG system, including morbidity trajectory and consistent high-cost users. He continues to work on multiple ACG System development projects including incorporating electronic medical records, developing opioid abuse markers, and refining frailty indicators.
Dr. Chang received his Doctor of Philosophy degree in 2009 from the Johns Hopkins Bloomberg School of Public Health under the supervision of one of the founders of the ACG System: Dr. Jonathan Weiner; while at the same time obtained a Masters degree in Biostatistics at Johns Hopkins. Prior to joining the Johns Hopkins University, he received a Master degree of Health Policy and Management from Harvard University.
Originally from Taiwan, Dr. Chang obtained his Bachelor degree of Public Health from the National Taiwan University in 1995, and then completed his first Master degree of Health and Welfare Policy at the National Yang-Ming University in 1997.
Melissa White Davis has more than 20 years of marketing, business development and strategic partnership experience. Her expertise includes building quantifiable marketing strategies, brand management, product marketing and navigating the evolving digital health and wellness industry. As the director of product marketing & business development for the population health analytics team, Ms. Davis is dedicated to advancing our mission to showcase the Johns Hopkins’ population health analytics capabilities to ultimately improve the health outcomes of patients and populations through the use of an analytics-driven interventions. In addition to driving the Johns Hopkins ACG system’s product marketing strategy, Ms. Davis also provides leadership and direction to the team’s market intelligence and business development activities.
Dr. Kharrazi is an Associate Professor of health policy and management at the Johns Hopkins School of Public Health (JHSPH), with a joint appointment in health informatics at the Johns Hopkins School of Medicine (JHSOM). He is the co-director of the JHSPH Center for Population Health IT (CPHIT) and is a core member of the Johns Hopkins ACG system’s R&D team. He is also the director of the DrPH Informatics track at JHSPH and the director of Health Informatics PhD and MSc programs at JHSOM.
Dr. Kharrazi has in-depth expertise with health information technology and population health analytics. His expertise includes assessing the needs and impact of health IT on care delivery, designing interoperable platforms for population health, and developing new methods for risk stratification. He has led several projects resulting in population health IT solutions that provide direct population‐based decision support to health care providers, insurers, and public health agencies.
Dr. Kharrazi’s research focuses on the application of informatics in population risk stratification and the effect of data type and quality in predicting utilization. He has published 90+ peer-reviewed journal articles and has been funded by a wide range of federal agencies such as NIH, ONC, AHRQ, FDA, CMS, and VHA. His recent projects include designing interoperable and Big Data platforms for population health analytics and evaluating advanced predictive models for risk stratification using a diverse set of data sources (EHRs, claims, geo-social determinants of health).
Dr. Kharrazi received a medical degree from Tehran/Iran University in Tehran, Iran. He received a master’s degree in Health Informatics from Dalhousie University in Halifax, Canada. He also holds an interdisciplinary PhD in Health Informatics from the same institution.
Dr. Pandya manages the operationalization of R&D work into the ACG System. As a health services researcher with clinical and informatics experience, Dr. Pandya’s research interests are at the intersection of health policy, clinical informatics, and the outcomes and quality of health care delivery. Broadly, he is interested in understanding the health systems and information technology drivers and determinants of high-quality care, and in particular, care for vulnerable populations, including the elderly and those that are at risk of health disparities.
Dr. Pandya joined the ACG Systems Team and Center for Population Health IT (CPHIT) as an Assistant Scientist in 2020. Previously, Dr. Pandya led a clinical analytics team in the Informatics and Analytics Department at Dana-Farber Cancer Institute, providing data analytics insights to various clinical care leaders to support better decision-making and improve quality of cancer care delivery. In addition, he has worked in the role of Senior Medical Informaticist for Wilmot Cancer Center and was Assistant Professor in the Department of Hematology/Oncology and Internal Medicine at University of Rochester Medical Center (URMC), leading their Data Analytics and Informatics program.
Dr. Pandya received a bachelor’s degree in Medicine and Surgery (MBBS) from Gujarat University in India. Following this, he received a master’s degree in Public Health from Stony Brook University and a PhD in Health Services Research and Policy from the University of Rochester, New York.
Dr. Hatef is an Assistant Scientist at the Center for Population Health IT (CPHIT) in the Department of Health Policy and Management at Johns Hopkins Bloomberg School of Public Health (JHSPH). She also serves as the academic director of the Johns Hopkins General Preventive Medicine Residency Program. As a preventive medicine-public health physician and clinical informatician, Dr. Hatef focuses on population health, social and behavioral determinants of health, and health information technology. Her main field of interest is the impact of social and behavioral determinants of health on health-related outcomes using health IT and Big Data. In collaboration with other faculty at CPHIT, she works on new methods of data mining and natural language processing to address social determinants of health (SDOH).
Among other projects at CPHIT, Dr. Hatef led the development of a population health framework in collaboration with Maryland Health Information Exchange’s Chesapeake Regional Information System for our Patients (CRISP) program, supported by the Maryland State Improvement Model and CMS. She also led a project assessing the impact of population-level SDOH on health care utilization at the Veterans Health Administration.
Dr. Hatef received her medical degree from Tehran University of Medical Sciences, in Tehran, Iran, and a master’s in public health from the JHSPH. Dr. Hatef completed a preliminary year in Internal Medicine at Yale-affiliated Griffin Hospital in Connecticut and the Preventive Medicine Residency at the JHSPH. She then completed the Clinical Informatics Practice Pathway at the same institution. Dr. Hatef is board certified in Preventive Medicine-Public Health and Clinical Informatics.
Sarah Kachur is part of the population health analytics leadership team at Johns Hopkins HealthCare Solutions. She is responsible for a portfolio of analytic products and engagements providing real world business analysis and actionable expertise within the institution and to Solutions’ external customers. Together with an experienced team of analysts and product managers, she develops data-driven patient and population-level solutions to address the most pressing clinical and financial challenges, leads analytic consulting engagements and plays a key decision-making role in the ACG System product management lifecycle and new product development.
Sarah’s career experience demonstrates a talent for strategy definition and realization using analytic insights across health plans and health systems. Prior to this role, she led Population Health Analytics for Johns Hopkins HealthCare’s four health plans and system-level population health initiatives, using her expertise in high-risk patient identification, clinical data mining, identification of health improvement opportunities, and monitoring and evaluating clinical programs. She is Johns Hopkins’ business lead for the precision medicine inHealth Population Health Center of Excellence, partnering with faculty to use advanced machine learning techniques to quantify changes in individual health and related utilization and expenditures. In addition, Sarah has 10 years of experience in managed care pharmacy, including Part D plan implementations, formulary and adherence program development, and risk prediction using pharmacy data.
Sarah received her M.B.A. from Loyola University Maryland. She holds a doctorate of pharmacy degree with a specialization in pharmacoeconomics and a bachelor’s degree in mathematics from the University of Rhode Island. She is board certified in ambulatory care pharmacy.
What is an “ACG®” or “Adjusted Clinical Group®“?
An Adjusted Clinical Group, or “ACG”, is an actuarial cell—dozens of which form the building blocks of the Johns Hopkins ACG System methodology. ACG categories are a series of mutually exclusive, health status categories defined by morbidity, age and sex, and they’re based on the premise that the level of resources needed for delivering appropriate health care to a population is correlated with the illness burden of that population.
ACG categories are a person-focused method of categorizing patients’ illnesses. Over time, each person develops numerous conditions. Based on the pattern of these morbidities, the ACG System approach assigns each individual to a single ACG category. Based on Barbara Starfield’s original research in the 1970s, this concept that clustering of morbidity is a better predictor of health services resource use than the presence of specific diseases is still the basis of the current ACG System methodology, and remains the fundamental concept that differentiates the ACG System from other case-mix adjustment methodologies today.
What is an “ADG®” or “Aggregated Diagnosis Group®“?
Aggregated Diagnosis Groups, or “ADGs”, are the first step of the ACG grouping logic. There are thousands of diagnosis codes that clinicians can use to describe patients’ health conditions, and each of these codes is assigned to one or more of 32 diagnosis groups, or “ADGs”. The ultimate goal of the ACG algorithm is to assign each person to a single morbidity group (an ACG), but there are 4.3 billion possible combinations of ADGs. So to create a more manageable number of unique combinations of morbidity groupings, the 32 ADGs are collapsed into 12 “CADGs” (Collapsed ADGs). ADGs and CADGs can be used for various analytic and research applications that don’t require mutually exclusive categories, such as multivariate predictive or explanatory models.