IT Solution for Population Health Management - Research Paper Example

IT Solution For Population Health Management IT Solution For Population Health Management Problem ment Management of population health is an issue of much importance to all nations making it a global issue. Even though it is important and critical in day to day life has certain challenges in it when it comes to data management. The methods used to collect data and its use has made the management of population health departments faces some difficulties. The emergence of the reform era that is dictated by ‘Pay- for- Value’ has made the health- care organizations to struggle. The organizations dealing with health care has found it had to compile and put the large data quantities in understandable structure. This has made it hard for the organizations to meet their set goals or targets. Even though the EHR (Electronic Health Records) provides many of the health facilities with clinical data in raw form, there have still been some challenges. The challenges include ensuring that the data is accessible and the same time usable. An additional challenge has been reporting the outcomes of the data that has been accessed and used (Health Catalysts, 2015). Alternative Analysis Improvement of Data Integration and Sharing Model According to WPRO, the integration of information systems in the health sector can be defined as, “the process of combining different components of health information systems so that they function in a more coordinated and unified manner” (Lum, Bennet & Whittaker, 2009). Information technology can be used to integrate and share the data in the health sector. The data integration can occur in different number of levels in the system of health information. These levels include: contents, data management, data quality assurance and analysis, organization support, data dissemination and sharing and donor inputs. The contents include the information or indicators while the data management involves the information technology use with a central repository. Quality of data assurance and analysis include the use of statistical and analytical techniques that are standardized. The organization support entails capacity building and training of the human resource. The sharing and dissemination of data is where there is the use of visual analytics. The donor inputs entail and not limited to, resource sharing. The art of integration of data entails effort being put in place to link data elements that are independent. The different sources of data that have different types of data or media of storage are integrated to develop new information. This is made possible through the use of information technology in the health facilities. Data integration covers all data handling aspects: collection, storage, assurance of quality and processing flow, compilation of data and analysis (Lum, Bennet & Whittaker, 2009). Strengths The first strength is that there is uniformity of the types of data that are used by the health centres as reference points. This solution also ensures that data found at a given sector or points are readily available for use. Integration of data ensures that there is no fund wastage as the facilities look for information that already exists (Lum, Bennet & Whittaker, 2009). Weaknesses The criteria laid much emphasis on the data collected and do not consider the quality of data collected. This might leave the data used in health facilities to have issues to do with quality, especially bias, computation errors and missing values. It does not show how the data will be used and the accessibility of the data to be used by the general public and the health professionals. The method does not include adjustments of data quality and reconciliation of the same. The criterion only focuses on the data integration and doesn’t lay emphasis on data use. The method also doesn’t address the transcriptional and computational errors that might have occurred at the collection point of the data (Lum, Bennet & Whittaker, 2009). Increase of Data Producers Analytical Skills Model The information technology, techniques can be used to improve the analytical skills of those who produce data at the health sectors. Through the use of information technology, it is certain that the project will take a short time and be cost effective at the same time. According to WHO, “…the health information systems in many low and middle-income countries tend to be “data-rich” but “information-poor” (Lum, Bennet & Whittaker, 2009). Data analysis can be defined as the process of raw data transformation thus adding value to statistical output. There is an increasing demand within the health sector for the information so as to meet the set goals. This means that capacity building is more important element to add to the technology that already exists. Their data producers’ analysis skills is therefore of much importance to the health sector. The skills to be increased include: census, modeling, projections and estimates, behavioral surveillance, response system and surveillance and health research. The additional skills entail births and deaths continuous monitoring, certification of the death cause, health accounts management of financial information and skills of data that are service- generated (Lum, Bennet & Whittaker, 2009). Strengths The data in the health sector will be analyzed fully and completely showing the details that are required. The quality and quantity of the data will improve to a greater extent. The data produced is reliable I most instances and can be used for planning. The data produced will be easy to understand thus ease in making use of it (Lum, Bennet & Whittaker, 2009). Weaknesses The model lays emphasis on the analytical skills and does not consider the collection point of data. Increasing the data producer’s analytical skills still gives space to biasness as data producer might lack ethics. The data produced can be politically influenced or influenced by other factors. This model is also not reliable since the data producers’ level of understanding differs and some might fail to grasp the important information (Lum, Bennet & Whittaker, 2009). Data Warehouse and Analytics Measurement System This employs the use of EDW (enterprise data warehouse) platform. Through this platform, one can build an analytical approach that is scalable and that will see that the data make sense and integrate. It involves the use of the software Late- Binding TM Data Warehouse that enables the health care facilities to automate the data processes. The processes include extraction, integration and aggregation of financial, administrative, clinical and other relevant data. It also allows for the application of advanced analytics that will enable the data organization. It is also important in the measurement of patient safety, clinical activities, outcomes and processes of patient satisfaction and costs involved (Health Catalysts, 2015). Strengths The data collected is reliable, easy to understand and employs both analyses of data and sharing of the same. It is an integration of the precious two alternatives in partially. The data collected is diverse and can be used is several ways. The system is flexible, allowing multiple uses ranging from applications, measurement, and extraction among others. The data analysis, presentation is advanced, accommodating the technological dynamics (Health Catalysts, 2015). Weaknesses It relies on the data provided from the primary source, thus still can’t be used to fully address the biasness and fabrication of data. The system relies on the human being thus may be prone to wrong data insertion thus giving wrong results. The data that are analyzed cannot be detected for any correction of fabrication that is in it (Health Catalysts, 2015). Selected Solution with Justification The selected solution for the population health data management problem is a Data Warehouse and Analytics Measurement System. This is because the alternative is partially more holistic compared to the other alternatives. It also makes full use of the opportunities that come with the information technology dynamics (Health Catalysts, 2015). SWOT Analysis The Strengths of the system include the reliability of the data produced. The automation part of it is also a major strength. The system can be used in different platforms, including clinical, administrative and financial sectors. The data produced is of high quality and the needed quantity. The data can be shared among different entities and compared using the system. The data produced and be kept and used later a long duration of time. The weaknesses of the system are that the information fed into the system can be fabricated information. The system does not have the power to detect the fabricated data and can make the information unreliable (Health Catalysts, 2015). The opportunities that lie within the system are that the automation can be improved so that the data will be analyzed as needed by the given entity. The dynamic of the information technology allows the system to be improved if need be (Health Catalysts, 2015). The system also allows it to be integrated easily with other sectors of the population health management system. The threats of the system are based on the fact that it relies so much on the computer softwares. This makes it prone for the data to be lost in case of computer or system server breakdown. The system is also prone to hacking and therefore the information can reach the wrong entity or can be tampered with (Lum, Bennet & Whittaker, 2009). Potential Impact to Clinical Workflow The Data Warehouse and Analytics Measurement System will ensure that the quality of data transferred in the workflow is sufficient and reaches all the relevant bodies. It will need the core team to be IT professionals with better understanding of the data. The comprehensiveness of the system might make the work flow take longer than expected. There will be no major changes on the workflow for the system fits in the workflow (Health Catalysts, 2015). Workflow of Population Health Management Solution Business Case Introduction Purpose The business idea here is to promote the use of data warehouse and analytics measurement system in the management of public health information. This will ensure that there is efficiency and effectiveness in the data that is collected in the health sectors. The system will also ensure that the data is reliable since it is not prone to uncontrollable manipulations. The data can also be stored for a long time and be availed to all the potential users through the website. The data can be distributed in other sectors that are interested. It is less time consuming and the analyzed data is attractive and of great quality (Byron, 2015). Scope and Distribution The solution can be used in all places in the world since all it requires is the acquisition of the software and human capital. It will be used in the health sectors, but can also be used in other sectors like finance among others. In the population health sector, it can be used in the administrative, financial, clinical and other sectors, as long as it entails data (Byron, 2015). Executive Summary Problem Statement Management of population health though important and critical in day to day life has certain challenges in it when it comes to data management. The methods used to collect data and its use has made the Population Health Management to face some difficulties. The emergence of the reform era that is dictated by ‘Pay- for- Value’ has made the health- care organizations to struggle. The organizations dealing with health care has found it had to compile and put the large data quantities in understandable structure. This has made it hard for the organizations to meet their set goals or targets. Even though the HER (Electronic Health Records) provide many of the health facilities with clinical data in raw form, there has still been some challenges (Health Catalysts, 2015). The challenges include ensuring that the data is accessible and the same time usable. An additional challenge has been reporting the outcomes of the data that has been accessed and used. Proposed Solution The proposed solution is the EDW (enterprise data warehouse) platform. Through this platform, one can build an analytical approach that is scalable and that will see that the data make sense and integrate. It involves the use of the software Late- Binding TM Data Warehouse that enables the health care facilities to automate the data processes (Health Catalysts, 2015). The processes include extraction, integration and aggregation of financial, administrative, clinical and other relevant data. It also allows for the application of advanced analytics that will enable the data organization. It is also important in the measurement of patient safety, clinical activities, outcomes and processes of patient satisfaction and costs involved. Recommendations Alternative Analysis Summary The alternative that is based on improvement of data integration and sharing is majorly focused on the data that is already within the sector. It proposes the use of information technology to see that this data is shared and integrated among the relevant sectors. The second alternative lays emphasis on the increase of the analytical skills of the data producers. This is done through trainings and other capacity building activities. It is important because it ensures that those who handle the data are professionals with the knowledge on how to analyze data. This enables the data produced to be of high quality and ensures it to elaborate enough. The data produced also addresses all the areas of importance. The last alternative is the Enterprise Data Warehousing solution which ensures that the data is scalable and can be integrated (Health Catalysts, 2015). The solution allows for the automation of the data processes. It also allows for advance analysis of the data that is within the health sectors. However, even though the alternative preferred is the use of Enterprise Data Warehousing solution, it is recommended that the other two solutions should be included in the data solution implementation. The integration will ensure that the implanted solution is holistic in nature addressing all the areas of concern (Health Catalysts, 2015). Recommended Design (Integration of the systems) Project Plan Summary The project aims to address the data problem experienced in the population health management sectors. This will be ensured through the implementation of the data warehouse and analytics measurement system. The system will be implemented in national, regional and local levels. There will be capacity building among those who are expected to be in day to day contact with the data system. The implementation of the project will be done on integration with the monitoring and evaluation tools. The monitoring and evaluation results will be used to make adjustments to the project to ensure that effectiveness and efficiency is fully achieved. The outcome expected is the improvement in population health data management (McAlearney, 2002). Financial Cost- Benefit Analysis with ROI Return of investment is a common economic calculation used to determine the investment attractiveness. Mathematically, it is calculated as; ROI= (Net Benefits/ Total Costs) Whereby the Net Benefits= Total Benefits- Total Costs. The benefit of the Enterprise Data Warehousing solution is the improvement of the quality of data in the population health centre. This in return will ensure that the data can be relied on during the planning. This reduces wastage costs on the part of the government and other funders of the health sector. The solution also improves the use of technology in the sector and the same time promotes capacity building in the health sector. The solution will also benefit the public through reduction of costs that are linked with duplication of activities that are related with the data management. This is because the solution will ensure that all the data are first hand collected and stored in for further use. The solution also will save the cost used in employing personnel every now and then to collect and analyze data. An additional benefit for the solution is that it can be used to handle data in different health sectors including clinical, financial and administrative sectors. There will be also a reduction in the cost of data analysis since it will be automated (Federal Geographic Data Committee, 2009). The total costs can be summarized inform on those who will lose the job in the form of unemployment since the solution introduces ease in data analysis. Additional costs will be in the form of installing the software and buying the technological equipment for the sector. There will be also cost in capacity building. As demonstrated, the gain far surpasses the costs, thus making the investment worth implementing (Federal Geographic Data Committee, 2009). Justification and Lost Opportunity Cost Compared to the other two alternatives, Enterprise Data Warehousing solution is more holistic. It directly tackles the issue of concern which is data reliability, contrary to the other alternatives. The lost opportunity cost is from the partial implementation of the two other alternatives. The two other alternatives are: Improvement of Data Integration and Sharing Model and Increase of Data Producers Analytical Skills Model. The cost is said to be partial since the selected option addresses some of the elements found in the two alternatives (Health Catalysts, 2015). Expected Benefits and Outcomes The expected benefits of the project are production of reliable data in the population health sector. The cost of the data analysis will also be reduced since the analysis tool is automated in the software. This is expected to reduce the number of human resources needed in data analysis thus saving the money for other activities in the sector. It is also expected that the data presentation will be more detailed and easy to understand. The data management will be centralized, thus reducing duplication of the data management activities. The data will be managed in a more comprehensive manner therefore ensuring accuracy of the data even after a long period of time (Federal Geographic Data Committee, 2009). References Byron, C. (2015). Population Health, Physician, Physical Leadership, and Quality. Population Health Management, 18 (1), 4-5. Federal Geographic Data Committee (2009). Economic Justification: Measuring Return on Investment (ROI) and Cost Benefit Analysis (CBA). Retrieved from https://www.fgdc.gov/policyandplanning/newspbp/EconomicJustification_ROI-CBA- Tutorial_v2_052809_FinalVersa.pdf Health Catalysts(2015). Population Health Management Solutions. Retrieved from https://www.healthcatalyst.com/population-health/ Lum, M., Bennet V. and Whittaker, M. (2009). Issues and Challenges for Health Information Challenges in the Pacific. Brisbane: University of Queensland. McAlearney, A. S. (2002). Population health management: Strategies to improve outcomes. Chicago: Health Administration Press. Read More